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Fine-tuning

Fine-tuning refers to the empirical observation in cosmology and physics that numerous fundamental constants and initial conditions of the universe exhibit extraordinarily precise values, enabling the formation of stable atoms, stars, galaxies, and the chemical complexity required for biological life.^[1] These parameters include the strengths of the four fundamental forces—gravitational, electromagnetic, weak nuclear, and strong nuclear—as well as dimensionless ratios such as the fine-structure constant (approximately 1/137), which governs electromagnetic interactions.^[1] For instance, a variation in the strong nuclear force by as little as 0.5% would prevent the binding of protons and neutrons into atomic nuclei, while even minor adjustments to the gravitational constant would either collapse the universe prematurely or inhibit star formation altogether.^[1] Prominent examples of this precision are outlined in analyses of key cosmological parameters, such as those highlighted by astrophysicist Martin Rees, who identified six critical numbers dictating the universe's large-scale structure and stability, including the ratio of electromagnetic to gravitational forces (approximately 10^40) and the density parameter Ω, which must lie within a narrow range near 1 for long-lived galaxies to exist.^[2] Such tuning is not merely qualitative; quantitative assessments reveal probabilities as low as 1 in 10^120 for certain constants, like the cosmological constant, aligning with observations from cosmic microwave background data and supernova surveys.^[1] This fine-tuning extends beyond constants to the universe's low entropy state at the Big Bang, as calculated by physicist Roger Penrose, where the required precision approaches 1 in 10^(10^123), far exceeding random chance under standard inflationary models.^[3] The phenomenon has sparked significant debate, with proponents of the fine-tuning argument viewing it as evidence for intentional design due to the causal improbability of these conditions arising without purpose, while critics invoke speculative mechanisms like the multiverse hypothesis—positing infinite universes with varying constants—to explain our universe's habitability via anthropic selection.^[4] However, the multiverse remains empirically unverified, lacking direct observational support and relying on untested extensions of quantum mechanics or inflation theory, whereas the fine-tuning data derives from well-established measurements in particle physics and cosmology.^[1] Despite these interpretations, the underlying empirical fact of fine-tuning is widely acknowledged by physicists across ideological spectrums, underscoring a profound puzzle in understanding the universe's causal origins.^[5]

Fundamentals

Definition and Process

Fine-tuning refers to the adaptation of a pre-trained machine learning model, typically a deep neural network, to a specific downstream task by continuing training on a smaller, task-specific dataset, thereby updating the model's parameters to improve performance while leveraging the general representations learned during initial pre-training.^[6]^[7] This approach, a form of transfer learning, contrasts with pre-training, which involves training from scratch on vast, often unlabeled datasets to capture broad patterns, as fine-tuning requires fewer resources—such as days of computation versus weeks or months—and focuses on labeled data for targeted refinement.^[7]^[8] The process begins with loading the pre-trained model's architecture and weights, which serve as an initialization point to avoid starting from random parameters.^[6]^[8] Task-specific modifications are then applied, such as adding a new output layer matched to the target dataset's classes (e.g., for classification tasks) or preparing input-output pairs for sequence prediction in language models.^[8] Hyperparameters are adjusted, notably using a smaller learning rate (e.g., 5e-5) for pre-trained layers to prevent overwriting established features, while higher rates may apply to newly added components.^[6]^[8] Subsequent steps include dataset preparation—collecting, cleaning, and formatting domain-specific data—and setting up the training environment with hardware like GPUs and batch sizes suited to the data volume.^[7] Training proceeds by iteratively updating parameters via gradient descent on the target data, often employing techniques like layer freezing (e.g., early convolutional or attention layers) to mitigate catastrophic forgetting, data augmentation for robustness, and validation on held-out sets using metrics such as cross-entropy loss.^[6]^[7]^[8] Post-training evaluation assesses generalization, with deployment following successful validation, potentially incorporating parameter-efficient variants like LoRA to limit updates to low-rank matrices and reduce memory demands to as low as 5.2 bits per parameter.^[7]

Comparison to Pre-Training and Transfer Learning

Pre-training involves initializing a neural network from random weights and training it on massive, diverse datasets—often unlabeled or self-supervised—to develop broad, generalizable representations of data patterns, such as linguistic structures in large language models trained on trillions of tokens from web corpora.^[9] This phase is computationally intensive, requiring extensive resources like thousands of GPUs over weeks or months, and is typically performed once by organizations with significant infrastructure, yielding foundational models like BERT or GPT series that capture world knowledge without task-specific objectives.^[10] In contrast, fine-tuning starts from these pre-trained weights and applies further supervised or reinforcement learning on smaller, curated datasets tailored to downstream tasks, such as classification or generation, using lower learning rates to refine parameters incrementally and achieve high performance with orders of magnitude less data and compute. This distinction enables fine-tuning to exploit pre-existing knowledge, reducing training time from months to hours or days, though it risks overfitting if the fine-tuning data lacks diversity.^[11] Fine-tuning represents a core implementation of transfer learning, the broader paradigm of reusing knowledge from a source domain or task to accelerate learning in a related target domain, often yielding superior results compared to training from scratch due to the inductive biases encoded in pre-trained features.^[12] Unlike feature extraction—a conservative transfer learning variant that freezes all pre-trained layers and trains only a lightweight classifier on top, preserving the base model's representations without modification—fine-tuning unfreezes and updates some or all layers, allowing deeper alignment to the target task but demanding techniques like learning rate scheduling to mitigate issues such as catastrophic forgetting, where task-specific updates erode general capabilities.^[13] Empirical studies in computer vision and natural language processing demonstrate that fine-tuning outperforms frozen transfer approaches by 5-20% in accuracy on benchmarks like GLUE or ImageNet subsets when target data is sufficient, though it requires validation to ensure the source and target domains share sufficient similarity. While pre-training emphasizes scale for emergent abilities like in-context learning, and transfer learning encompasses both inductive (feature reuse) and transductive (domain adaptation) strategies, fine-tuning bridges them by enabling efficient specialization; for instance, models pre-trained on general text can be fine-tuned for medical question-answering with datasets under 100,000 examples, achieving near-state-of-the-art results unattainable via pre-training alone due to data scarcity in niche domains.^[14] This hierarchy—pre-training as foundational, transfer learning as conceptual framework, and fine-tuning as operational technique—has driven advancements since the 2010s, with parameter-efficient variants like LoRA further distinguishing fine-tuning by updating low-rank adapters rather than full weights, reducing costs by 90-99% while approximating full fine-tuning efficacy.^[15]

Historical Development

Early Foundations in Machine Learning

The concept of fine-tuning originated as a practical extension of transfer learning in early neural network research, where models trained on one task were adapted to related tasks by further training on smaller datasets, leveraging previously learned representations to mitigate data scarcity and computational constraints. In 1976, Stevo Bozinovski and Ante Fulgosi introduced the first documented method of transfer learning in neural networks, initializing a target network's weights with those from a source network trained on a primary task and then continuing training on the target task data.^[16] ^[17] This approach demonstrated empirical gains in performance for pattern recognition tasks, as the transferred weights provided a better starting point than random initialization, reducing training time and improving convergence in resource-limited environments of the era.^[18] During the 1980s, as backpropagation enabled training of multi-layer networks, similar adaptation techniques appeared in applications like adaptive filtering and control systems, where initial training on general patterns was followed by task-specific adjustments to refine weights without full retraining. For instance, the MADALINE network, first implemented in the 1960s but refined in subsequent decades, used weight updates to adapt to real-world signal processing, foreshadowing fine-tuning's role in incremental learning.^[19] These methods highlighted causal benefits: pre-training captured robust features transferable across domains, while fine-tuning aligned them to downstream specifics, avoiding catastrophic forgetting through gradual parameter updates. Early limitations included sensitivity to domain shifts, where dissimilar source and target distributions led to negative transfer, as observed in initial experiments requiring careful selection of related tasks.^[20] By the early 1990s, transfer learning formalized these practices amid growing interest in domain adaptation, with researchers exploring inductive biases in neural architectures to facilitate knowledge reuse. Surveys of the period trace roots to these foundational works, noting that while computational power constrained scale, the principle of parameter continuation established fine-tuning's efficacy for tasks like handwriting recognition and early computer vision, where adapting shallow networks yielded measurable accuracy improvements over isolated training.^[20] This era's empirical focus—prioritizing verifiable performance metrics over theoretical universality—laid groundwork for later scalability, though adoption remained niche due to the dominance of task-specific models until data abundance in the 2000s.

Rise in Deep Learning (2010s)

The resurgence of deep learning in the early 2010s, catalyzed by the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), highlighted the efficacy of convolutional neural networks (CNNs) trained on massive datasets. AlexNet, developed by Krizhevsky, Sutskever, and Hinton, achieved a top-5 error rate of 15.3% on the ILSVRC-2012 validation set, surpassing the runner-up's 26.2% and demonstrating the advantages of deep architectures over shallower models.^[21] The model's training involved pre-training on the broader ImageNet dataset (1.2 million images across 1000 classes) followed by fine-tuning on the ILSVRC subset, which reduced the error to 16.6%, establishing fine-tuning as a practical method to adapt resource-intensive deep models to specific tasks amid limited labeled data for downstream applications.^[21] In computer vision, fine-tuning became a standard practice post-AlexNet, enabling adaptation of ImageNet-pre-trained CNNs like VGG (2014) and ResNet (2015) to domains with scarce data, such as medical imaging or object detection, by updating only upper layers while freezing lower ones to retain generic features. Yosinski et al. (2014) empirically demonstrated this layered transferability: early-layer neurons encode general visual patterns transferable across datasets, whereas later layers capture task-specific representations, with transfer performance degrading as distance between source and target tasks increases; their experiments on AlexNet variants showed that fine-tuning top layers alone could boost accuracy by up to 10% on small target sets compared to random initialization.^[22] This insight informed efficient strategies, reducing computational demands—training deep nets from scratch required GPU weeks and millions of examples—while surveys of the era document over 50 deep transfer learning approaches emerging by the late 2010s, emphasizing instance, feature, and parameter transfer via fine-tuning.^[23] Toward the decade's end, fine-tuning extended prominently to natural language processing (NLP) with transformer architectures. The 2018 BERT model, pre-trained on 3.3 billion words via masked language modeling and next-sentence prediction, achieved state-of-the-art results on 11 NLP tasks after fine-tuning with minimal task-specific layers, outperforming prior methods by 5-10% on benchmarks like GLUE (aggregate score of 80.5 vs. previous 75.0).^[24] BERT's bidirectional pre-training and straightforward fine-tuning paradigm shifted NLP from hand-engineered features to scalable transfer learning, influencing subsequent models and solidifying fine-tuning as a core technique for adapting large pre-trained encoders to classification, question answering, and other tasks with limited supervision.^[24] This evolution reflected broader 2010s trends in deep learning, where fine-tuning mitigated data and compute bottlenecks, enabling widespread application beyond vision to sequential data domains.^[25]

Scaling with Large Models (2020s)

The release of large language models (LLMs) with hundreds of billions of parameters, such as OpenAI's GPT-3 in June 2020 featuring 175 billion parameters, intensified the challenges of fine-tuning due to escalating computational demands; full parameter updates required processing datasets on clusters with thousands of GPUs, often costing millions in resources and limiting accessibility beyond major organizations. This scale shifted focus toward methods that preserved pre-trained weights while adapting models efficiently, enabling broader experimentation and deployment without retraining from scratch. Parameter-efficient fine-tuning (PEFT) techniques proliferated to mitigate these barriers, prioritizing updates to a minimal subset of parameters—typically under 1% of the total—while freezing the base model. Low-Rank Adaptation (LoRA), introduced in a March 2021 paper by Microsoft researchers, exemplified this by decomposing weight update matrices into low-rank factors inserted into transformer layers, reducing trainable parameters by orders of magnitude and matching full fine-tuning performance on tasks like natural language generation with 10,000 times less memory.^[26] Building on adapter concepts from the 2010s, variants like Houlsby adapters were scaled for LLMs, adding lightweight bottleneck modules parallel to attention and feed-forward layers, which proved effective for domain adaptation in models up to 11 billion parameters by mid-decade.^[27] These approaches empirically demonstrated that performance gains scaled with model size when compute was allocated to targeted updates rather than exhaustive retraining, as validated in benchmarks showing near-equivalent downstream accuracy with reduced overhead.^[26] Instruction tuning emerged as a scaling strategy in 2021–2022, involving fine-tuning on curated datasets of diverse task instructions and responses to enhance generalization; Google's FLAN method, applied to the 137-billion-parameter PaLM model in 2022, boosted zero-shot performance by over 18 points on average across 50+ benchmarks through cross-task data mixing, illustrating how instructional data volume correlated with emergent capabilities in larger architectures. Concurrently, Reinforcement Learning from Human Feedback (RLHF) integrated with supervised fine-tuning in OpenAI's InstructGPT (January 2022), which adapted a 175-billion-parameter GPT-3 variant using proximal policy optimization on human-ranked outputs, yielding safer and more helpful responses as measured by preference win rates exceeding 70% over base models.^[28] Quantized extensions like QLoRA (May 2023) further enabled fine-tuning of 65-billion-parameter models on single consumer GPUs by combining 4-bit quantization with LoRA, cutting memory use to 48 GB while preserving 16-bit training fidelity on tasks like question answering. By 2023–2025, these innovations underpinned widespread adoption in open-source ecosystems, with models like Meta's LLaMA series (7–70 billion parameters, February 2023) fine-tuned via PEFT for specialized applications, achieving state-of-the-art results on leaderboards such as Hugging Face's Open LLM with adapters consuming under 1% additional parameters. Empirical scaling analyses confirmed that optimal learning rates and batch sizes in fine-tuning followed power laws with model size and dataset scale, predicting loss reductions proportional to compute investment and guiding efficient resource allocation for trillion-parameter regimes. However, persistent limitations included catastrophic forgetting in PEFT, where task-specific gains degraded base model versatility, necessitating hybrid full-PEFT pipelines for production-scale models exceeding 100 billion parameters. Deployments like ChatGPT (November 2022), fine-tuned from GPT-3.5 via RLHF, demonstrated practical scalability, handling millions of users while aligning outputs to empirical human judgments over raw pre-training predictions.

Core Techniques

Supervised Fine-Tuning

Supervised fine-tuning (SFT) involves adapting a pre-trained large language model (LLM) by training it on a curated dataset of labeled input-output pairs, typically consisting of prompts and corresponding desired responses, using standard supervised learning objectives such as cross-entropy loss.^[28] This process leverages the general knowledge encoded during pre-training while steering the model toward specific behaviors, such as instruction-following or domain-specific task performance, by minimizing prediction errors on the fine-tuning data.^[29] The dataset is usually smaller than pre-training corpora, often comprising thousands to tens of thousands of high-quality examples generated by human annotators who provide responses to diverse prompts.^[30] In practice, SFT employs gradient descent optimization on the model's parameters, with hyperparameters like learning rate schedules (e.g., cosine decay) and training epochs tuned to avoid excessive deviation from the pre-trained weights. For instance, in the development of InstructGPT, a GPT-3 model was fine-tuned on approximately 13,000 demonstration examples for 16 epochs, resulting in improved alignment with user intents across tasks while preserving much of the base model's capabilities.^[28] This step often precedes more advanced alignment techniques, serving as a foundational adaptation that enhances the model's utility for downstream applications by conditioning it to generate coherent, task-relevant outputs rather than raw next-token predictions.^[31] High-quality SFT datasets emphasize diversity in prompts—covering reasoning, creativity, and factual recall—to mitigate biases inherent in the annotation process, though the reliance on human-generated labels introduces potential inconsistencies or domain limitations.^[32] Empirical evaluations, such as those in instruction-tuning benchmarks, demonstrate that SFT can yield substantial gains in metrics like task success rates (e.g., 20-30% improvements in instruction adherence) but requires careful data curation to prevent overfitting to narrow patterns in the training set.^[31] Recent implementations, including those for models like LLaMA variants, have scaled SFT to incorporate synthetic data augmentation, yet human oversight remains critical for ensuring response quality and reducing hallucinations.^[30] Overall, SFT's efficacy stems from its causal mechanism of updating weights to prioritize high-reward trajectories in the data distribution, though its outcomes are bounded by the fidelity and representativeness of the supervisory signals provided.^[28]

Reinforcement Learning from Human Feedback

Reinforcement Learning from Human Feedback (RLHF) is a fine-tuning method that aligns large language models with human preferences by treating response generation as a reinforcement learning problem, where a reward signal derived from human judgments guides policy optimization.^[28] Introduced prominently in OpenAI's InstructGPT system in January 2022, RLHF builds on supervised fine-tuning by addressing limitations in directly optimizing for complex, subjective human values that supervised data alone cannot capture.^[33] The approach has since become standard for deploying instruction-following models, including GPT-3.5 and derivatives, enabling outputs that are more helpful, less verbose, and reduced in toxicity compared to base models of similar scale.^[28] The RLHF pipeline consists of three main stages. First, a language model undergoes supervised fine-tuning (SFT) on a dataset of prompts paired with high-quality human-written responses to establish a baseline for instruction adherence.^[34] Second, human annotators rank multiple model-generated completions for the same prompt, typically preferring outputs that are more helpful, honest, and harmless; these pairwise comparisons form a preference dataset used to train a separate reward model, often a fine-tuned version of the SFT model, to scalar-score responses based on predicted human approval.^[28] Third, reinforcement learning optimizes the policy— the language model itself—using an algorithm like Proximal Policy Optimization (PPO) to maximize expected reward, subject to a Kullback-Leibler (KL) divergence penalty against the SFT reference model to mitigate reward hacking and preserve capabilities.^[28] This KL regularization, typically weighted at 0.01-0.1 in implementations, prevents excessive deviation that could degrade performance on unseen tasks.^[34] In practice, RLHF requires substantial computational resources and human labor: OpenAI's InstructGPT experiments involved approximately 30-40 thousand preference pairs collected via crowdworkers, with reward model training on models up to 1.3 billion parameters and PPO fine-tuning on GPT-3-scale models demanding thousands of GPU-hours.^[28] Empirical results from the 2022 InstructGPT evaluation showed RLHF-tuned models outperforming their 175-billion-parameter pre-trained counterparts by 10-20% on human-rated instruction-following across diverse tasks, including summarization and creative writing, while exhibiting lower rates of hallucinations in factual queries.^[28] However, the method's efficacy depends on the quality of human feedback; annotator agreement on preferences averages around 60-70% in reported datasets, introducing noise that can propagate biases, such as over-optimization for sycophantic or overly cautious responses.^[35] Despite these gains, RLHF faces inherent limitations in scalability and robustness. Human annotation costs scale poorly for models exceeding trillions of parameters, prompting alternatives like reinforcement learning from AI feedback (RLAIF), though these risk amplifying reward model errors.^[36] RLHF can induce mode collapse, where models generate less diverse outputs to exploit reward patterns, reducing creativity; studies post-InstructGPT observed up to 50% drops in output entropy after PPO iterations.^[37] Moreover, since rewards proxy preferences rather than objective truth, RLHF prioritizes perceived helpfulness over factual accuracy, potentially reinforcing subjective or culturally biased judgments from annotators, who in OpenAI's case were primarily U.S.-based contractors.^[38] Variants like direct preference optimization (DPO), introduced in 2023, bypass explicit reward modeling by jointly optimizing policy and preferences, offering computational efficiency while approximating RLHF outcomes on benchmarks like MT-Bench.^[34]

Parameter-Efficient Methods

Parameter-efficient fine-tuning (PEFT) methods adapt large pre-trained models by modifying or adding only a small subset of parameters, often less than 1% of the total, while freezing the majority of the model's weights to minimize memory and computational requirements.^[39] These approaches address the resource demands of full fine-tuning, which scales quadratically with model size due to gradient computations and optimizer states, enabling deployment on consumer hardware for models exceeding billions of parameters.^[26] PEFT techniques preserve the base model's generalization while achieving task-specific performance comparable to full fine-tuning in many cases, as demonstrated across natural language processing benchmarks.^[40] One foundational PEFT category involves additive parameter insertions, such as adapter modules. Introduced by Houlsby et al. in 2019, adapters consist of small feed-forward networks—typically bottleneck layers with down-projection and up-projection matrices—inserted parallel to the original transformer layers, with only these modules trained during adaptation.^[27] For a BERT-base model with 110 million parameters, adapters add approximately 3 million trainable parameters (about 3%), yet match or exceed full fine-tuning performance on GLUE tasks while reducing trainable parameters by over 90%.^[27] Variants like Houlsby-style adapters place modules after attention and feed-forward sublayers, optimizing for modularity and task-specific stacking without interference.^[39] Prompt-based PEFT methods optimize lightweight, continuous representations prepended to inputs or attention mechanisms, avoiding architectural changes. Prefix-tuning, proposed by Li and Liang in 2021, generates task-specific prefixes for the key and value projections in each transformer layer, training only these prefixes (e.g., 0.1% of GPT parameters for generation tasks) while keeping the language model frozen.^[41] On tasks like summarization and dialogue generation with GPT-2 and T5 models, prefix-tuning outperforms full fine-tuning in parameter efficiency, using 0.03% to 0.05% trainable parameters and reducing GPU memory by up to 37 times.^[41] Related techniques, such as prompt tuning, extend this by optimizing soft prompts solely at the input layer, effective for models over 10 billion parameters but less so for smaller ones due to limited expressivity.^[39] Low-rank adaptation (LoRA), developed by Hu et al. in 2021, approximates weight updates in query, key, value, and output projections as low-rank decompositions: \Delta W = BA, where B and A are low-rank matrices with rank r \ll \min(d_{in}, d_{out}), injecting these into frozen layers.^[26] For GPT-3's 175 billion parameters, LoRA trains just 0.01% of parameters, achieving 99% of full fine-tuning performance on RoBERTa GLUE tasks and enabling downstream adaptation with 3,000 times fewer trainable parameters and no inference latency overhead after merging.^[26] LoRA's efficacy stems from the observation that fine-tuning updates exhibit low intrinsic dimensionality, often rank 1-8 suffices for near-optimal adaptation.^[26] Extensions like quantized LoRA (QLoRA) further enhance efficiency by combining LoRA with 4-bit NormalFloat quantization of the base model, using double quantization and paged optimizers to manage memory spikes. QLoRA, as detailed in implementations for LLaMA models, fine-tunes a 65-billion-parameter model on a single 48GB GPU, reducing memory from over 780GB (full precision full fine-tuning) to 24GB while preserving perplexity within 0.1 points of 16-bit baselines. Empirical evaluations show QLoRA maintains downstream task accuracy, such as 50.1% on Vicuna benchmarks, versus full methods, underscoring PEFT's role in democratizing large model adaptation amid hardware constraints. Surveys categorize PEFT into additive, selective, and reparameterization-based families, with ongoing research addressing continual learning and multimodal extensions.^[40]

Applications and Use Cases

In Natural Language Processing

Fine-tuning pre-trained language models has enabled significant advancements in natural language processing tasks, particularly by adapting general-purpose representations to domain-specific or task-oriented requirements with relatively small labeled datasets.^[42] In text classification, such as sentiment analysis, models like BERT are fine-tuned on benchmarks including SST-2, where they achieve accuracies exceeding 95%, outperforming non-fine-tuned baselines by leveraging contextual embeddings for nuanced polarity detection.^[24] Similarly, for named entity recognition, fine-tuning transformer-based models on datasets like CoNLL-2003 yields F1 scores around 93-95%, as the added task-specific layers refine entity boundary and type predictions without retraining from scratch.^[43] Machine translation benefits from fine-tuning large language models on parallel corpora, where even 32 training instances can produce translations rivaling dedicated systems, with BLEU scores improving by 5-10 points over zero-shot prompting in low-resource languages. Abstractive summarization tasks, such as those in the CNN/Daily Mail dataset, see enhanced ROUGE scores post-fine-tuning, with models generating coherent summaries equivalent to human references and outperforming foundation models by approximately 10% in factual consistency metrics. Question answering on datasets like SQuAD demonstrates fine-tuned models extracting answers with exact match accuracies over 90%, as the process aligns the model's attention mechanisms to passage-question dependencies.^[24] In generative tasks, fine-tuning GPT-series models on instruction-following datasets improves coherence and relevance in dialogue systems, reducing hallucination rates by 20-30% compared to pre-trained outputs, though performance varies by prompt complexity.^[42] These applications underscore fine-tuning's efficiency in resource-constrained settings, often requiring only hours of GPU time versus weeks for full training, while maintaining generalization across NLP subtasks like entailment and coreference resolution.^[44] Empirical evaluations on GLUE and SuperGLUE benchmarks confirm that fine-tuned models consistently surpass prior SOTA by 5-15% across aggregated scores, highlighting the technique's role in bridging pre-training generality with task precision.^[45]

In Computer Vision and Multimodal Tasks

Fine-tuning pre-trained vision models has become a standard practice in computer vision tasks, enabling adaptation from large-scale datasets like ImageNet to downstream applications such as image classification, object detection, and semantic segmentation.^[46] For instance, models like Vision Transformers (ViTs), initially pre-trained on billions of images, achieve significant performance gains when fine-tuned on task-specific data, often surpassing training from scratch by leveraging transferable hierarchical features.^[47] Empirical evaluations across 31 image recognition datasets demonstrate that full fine-tuning with optimizers like SGD can yield accuracies exceeding 90% on benchmarks like CIFAR-100, while parameter-efficient variants reduce computational costs without substantial loss in efficacy.^[47]^[48] Parameter-efficient fine-tuning (PEFT) methods, including adapters and low-rank adaptations (LoRA), have gained prominence for vision tasks by updating only a fraction of parameters—typically under 1%—while maintaining near full fine-tuning performance on dense prediction tasks like panoptic segmentation.^[49]^[50] These approaches are particularly effective in resource-constrained settings, as shown in studies where adapter-based tuning on video recognition datasets improved mean average precision (mAP) by 5-10% over frozen backbones, with training times reduced by orders of magnitude compared to full updates.^[48] In object detection, reward-based fine-tuning has empirically boosted models like DETR on COCO datasets, achieving up to 2-3 points higher AP scores by aligning predictions with task-specific objectives.^[50] In multimodal tasks, fine-tuning extends to vision-language models (VLMs) that integrate image encoders with language decoders, enabling capabilities like visual question answering (VQA) and image captioning.^[51] Pre-trained VLMs such as Qwen2-VL or LLaVA, initialized on vast image-text corpora, are fine-tuned using supervised datasets with instruction-response pairs, resulting in improved zero-shot generalization; for example, fine-tuning LLaVA-1.5 on 558k filtered examples enhanced VQA accuracy on ScienceQA by 15-20% over base models.^[51]^[52] Techniques like reinforcement learning from task descriptions further refine VLMs for decision-making, as demonstrated in frameworks that elevate performance on multimodal benchmarks without extensive data augmentation.^[53] Applications include domain-specific adaptations, such as fine-tuning Phi-3-vision for medical imaging analysis, where customized datasets yield precise anomaly detection with mAP improvements of 10% on specialized corpora.^[54] Despite these advances, empirical studies highlight trade-offs in multimodal fine-tuning, where PEFT methods on VLMs preserve 95% of full fine-tuning accuracy on vision tasks but require careful hyperparameter tuning to mitigate feature drift in cross-modal alignments.^[55] Overall, fine-tuning in CV and multimodal contexts has driven practical deployments in areas like autonomous systems and content moderation, with results consistently showing 5-15% relative gains in task metrics across diverse evaluations.^[50]^[56]

Specialized Domains

Fine-tuning large language models (LLMs) for specialized domains adapts pre-trained models to fields requiring precise terminology, regulatory compliance, and task-specific expertise, such as healthcare, law, and finance, often yielding performance gains over general-purpose models on domain benchmarks.^[57] Techniques like supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and parameter-efficient methods such as QLoRA enable this adaptation while mitigating computational demands; for instance, QLoRA reduces memory usage from 780 GB to 48 GB when fine-tuning a 65-billion-parameter Llama model.^[58] In healthcare, fine-tuned LLMs support clinical tasks including report generation and patient data analysis. EchoGPT, fine-tuned from Llama-2 using QLoRA on 95,506 echocardiography reports, produced summaries rated by four board-certified cardiologists as comparable to human experts in completeness, conciseness, correctness, and clinical utility.^[58] Similarly, CohortGPT, built on GPT-4 with chain-of-thought prompting and RLHF, screened thousands of radiology reports for clinical trial eligibility, achieving reliable disease classification on datasets like Indiana chest X-ray and MIMIC-CXR.^[58] LlamaCare, fine-tuned for electronic health record (EHR) integration, handles discharge summaries and mortality prediction, demonstrating improved domain relevance over base models.^[58] These applications highlight fine-tuning's role in enhancing accuracy for high-stakes diagnostics, though challenges persist in long-context understanding and ethical data handling.^[59] In the legal domain, fine-tuning targets contract review, case analysis, and compliance, where models must interpret nuanced statutes and precedents. Harvey AI, in partnership with OpenAI, developed a custom-trained model on case law datasets to automate complex tasks like document drafting and research, outperforming generic LLMs in relevance and precision for legal workflows.^[60] Domain-adapted models using embedding fine-tuning and retrieval-augmented generation have shown up to 30% higher identification of relevant content in benchmarks compared to standard methods.^[61] Such adaptations address the limitations of general LLMs in handling jurisdiction-specific language, though transparency and bias in training data remain concerns.^[57] For finance, fine-tuning via continual pre-training on sector-specific corpora improves sentiment analysis, fraud detection, and market forecasting. Adapted GPT-4 variants excel in predicting financial trends by incorporating proprietary transaction data, surpassing base models in domain benchmarks due to enhanced handling of numerical and temporal patterns.^[57] Instruction fine-tuning on financial reports reduces errors in regulatory compliance tasks, with studies noting consistent gains in accuracy for tasks like risk assessment.^[62] Challenges include sourcing high-quality, non-public datasets and ensuring models adhere to financial regulations amid volatile market dynamics.^[57] Beyond these, fine-tuning extends to scientific domains like protein structure prediction and climate modeling, where models trained on specialized corpora—such as molecular biology texts—accelerate hypothesis generation, though empirical validation against experimental data is essential to avoid hallucination risks.^[7] Overall, domain-specific fine-tuning prioritizes causal task alignment over broad generalization, enabling verifiable performance uplifts in controlled evaluations.^[62]

Challenges and Technical Limitations

Resource Demands and Efficiency Issues

Fine-tuning large language models via full parameter updates demands substantial computational resources, including high memory for storing model weights, gradients, and optimizer states, often exceeding capacities of consumer-grade hardware. For example, naively fine-tuning the Llama-2 7B model requires approximately 110 GB of RAM, rendering it infeasible on typical single GPUs without advanced techniques like quantization or model parallelism.^[63] Larger models, such as those in the 70B parameter range, typically necessitate clusters of multiple high-end GPUs, with Llama 2 variants requiring a minimum of four NVIDIA GPUs to accommodate the combined needs of forward/backward passes and state maintenance.^[64] Efficiency bottlenecks extend beyond memory to include low GPU utilization rates, frequently limited by memory bandwidth rather than compute capacity or FLOPs throughput. During training, attention mechanisms and data loading can saturate bandwidth before fully exploiting GPU cores, resulting in utilization below 50% in many setups despite available hardware.^[65] ^[66] Cloud-based fine-tuning exacerbates costs, with hourly rates for GPU clusters making iterative experimentation prohibitively expensive for non-enterprise users, often prompting reliance on parameter-efficient methods.^[67] Parameter-efficient fine-tuning (PEFT) approaches, such as LoRA and adapters, address these issues by updating only a fraction of parameters—typically 0.1-1%—while freezing the base model, slashing memory needs by up to 3-4x and enabling execution on single GPUs with 16-24 GB VRAM for models up to 7B parameters.^[39] ^[68] These methods yield 50-70% reductions in overall fine-tuning costs compared to full updates, though they introduce minor overhead from adapter computations and may underperform on tasks requiring deep structural changes.^[68] ^[69] Despite such efficiencies, scaling PEFT to billion-parameter models still demands specialized hardware, and full fine-tuning remains computationally overwhelming for most applications due to its quadratic growth in resource scaling with model size.^[70]

Overfitting, Forgetting, and Generalization Problems

Fine-tuning pre-trained models, particularly large language models (LLMs), often encounters overfitting, where the model excessively memorizes task-specific training data at the expense of broader applicability, leading to degraded performance on unseen examples. This issue arises prominently when adapting massive pre-trained models to limited downstream datasets, as the high parameter count amplifies sensitivity to noise or idiosyncrasies in the fine-tuning data. Empirical studies demonstrate that full fine-tuning techniques can reduce benchmarking performance across models due to mismatched data distributions and insufficient regularization, exacerbating overfitting even in tasks like automated evaluation.^[71] In reinforcement learning-based fine-tuning, models may overfit to specific prompts, assigning inflated probabilities to trained sequences while faltering on variations, as observed in controlled experiments with LLMs.^[72] Catastrophic forgetting, or the abrupt loss of pre-trained knowledge during fine-tuning on new tasks, further compounds these challenges by overwriting foundational representations without rehearsal of prior data. This phenomenon is empirically verified in LLMs spanning 1 billion to 7 billion parameters, where continual fine-tuning on sequential tasks results in significant accuracy drops on original capabilities, such as factual recall or reasoning benchmarks.^[73] However, scaling to larger models, like 70 billion parameters, mitigates forgetting severity, suggesting that model capacity influences plasticity-stability trade-offs, though smaller models remain vulnerable in resource-constrained settings.^[73] Recent analyses confirm that fine-tuning LLMs on single tasks induces forgetting of pre-training knowledge, compromising multi-domain effectiveness unless mitigated by techniques like elastic weight consolidation.^[74] These issues culminate in generalization problems, where fine-tuned models exhibit brittle out-of-distribution (OOD) performance despite strong in-domain results. Classification-oriented fine-tuning often transfers positively across domains, preserving utility, whereas generation tasks frequently induce negative transfer, hindering adaptation to novel contexts or complexities like spatial reasoning.^[75] Overfitting and forgetting jointly erode generalization by narrowing the model's inductive biases toward fine-tuning artifacts, as evidenced in vision-language models where prompt sensitivity and adapter scalability limit cross-task robustness.^[76] Empirical investigations highlight that while instruction-tuned LLMs generalize adequately on simple tasks, performance degrades markedly on intricate ones, underscoring the need for data diversity and regularization to approximate causal invariances beyond spurious correlations in training sets.^[77]

Safety, Alignment, and Controversies

Risks of Undermining Model Safety

Fine-tuning large language models (LLMs) can undermine pre-existing safety alignments by altering the model's learned refusal behaviors and increasing susceptibility to generating harmful content. Safety mechanisms, often established through reinforcement learning from human feedback (RLHF), prioritize refusing queries involving violence, hate speech, or illegal activities; however, fine-tuning on task-specific data can override these by optimizing for new objectives that conflict with safety constraints, such as improved helpfulness or domain adaptation.^[78] This degradation occurs because fine-tuning adjusts model parameters to minimize loss on the new dataset, potentially eroding the high-dimensional representations that encode safe responses.^[79] A primary risk involves the introduction of adversarial or harmful examples in the fine-tuning dataset, which can systematically subvert alignment with minimal effort. Studies demonstrate that incorporating as few as 10 malicious data points suffices to disrupt safeguards in models like Llama-2-7B, enabling outputs that assist in disallowed tasks such as creating explosives or phishing attacks, at a computational cost far lower than initial training.^[79] Even parameter-efficient techniques, such as low-rank adaptation (LoRA), fail to mitigate this, as they still propagate adversarial gradients that weaken refusal rates from over 90% to below 20% on red-teaming benchmarks.^[78] Beyond deliberate attacks, inadvertent safety erosion arises from benign fine-tuning datasets aimed at enhancing utility, such as instruction-following corpora that inadvertently include edge cases or noisy data conflicting with safety priors. For instance, fine-tuning on popular datasets for responsiveness can increase jailbreak vulnerability by a factor of three or more, as the model learns to prioritize compliance over caution, leading to higher rates of toxic or insecure responses.^[80] This effect stems from optimization dynamics where safety signals, being sparse in downstream data, are outcompeted by task-specific gradients, resulting in emergent behaviors like hallucinated harmful instructions.^[78] Broader implications include heightened proliferation risks, as accessible fine-tuning tools democratize customization but amplify misuse potential without robust safeguards. Models post-fine-tuning exhibit reduced robustness to prompt manipulations, with empirical tests showing over 22-fold increases in harmful response likelihood compared to base aligned versions, underscoring the fragility of current alignment pipelines.^[81] These vulnerabilities persist across architectures, highlighting a causal gap between fine-tuning's flexibility and sustained safety enforcement.^[79]

Empirical Evidence on Alignment Degradation

Empirical studies have consistently demonstrated that fine-tuning pre-aligned large language models (LLMs) can erode safety mechanisms, leading to increased generation of harmful or unsafe outputs, even when the fine-tuning dataset consists solely of benign examples. For instance, a 2023 analysis of models such as Llama-2-7B-chat revealed that instruction fine-tuning on non-adversarial data significantly reduced refusal rates for harmful queries, with red-teaming evaluations showing up to a 10-fold increase in compliance with unsafe prompts post-fine-tuning.^[78] This degradation occurs because fine-tuning shifts model representations away from the safety subspace established during initial alignment, prioritizing task-specific performance over generalized harmlessness.^[78] Further evidence from 2024 experiments on GPT-3.5 Turbo and Llama-2 variants indicated that incorporating just 10 harmful examples into fine-tuning data—representing a minimal fraction of the dataset—caused models to produce disallowed content in over 80% of evaluated harmful scenarios, compared to near-zero rates in the base aligned models.^[79] Even without explicit harmful data, fine-tuning on standard instruction corpora has been shown to amplify jailbreak vulnerability by a factor of three and elevate harmful response likelihood by over 22 times, as measured across thousands of adversarial prompts in security benchmarks.^[81] A 2025 study examining Llama, Mistral, and GPT-3.5 Turbo models confirmed this pattern, attributing safety collapse to distributional mismatches between alignment datasets (rich in refusal patterns) and fine-tuning data (task-focused without safety reinforcement), resulting in representational drift that weakens guardrails.^[82] Quantitatively, post-fine-tuning models exhibited refusal rates dropping from 95% to below 50% on safety benchmarks like HarmfulQA, while maintaining or improving benign task performance, highlighting a trade-off where utility gains come at the expense of robustness against misuse. These findings underscore that fine-tuning disrupts the latent safety structures in LLMs, often irreversibly without targeted interventions like safety-specific regularization.^[83]

Debates: Innovation vs. Overregulation

Fine-tuning of AI models has sparked contention between advocates prioritizing unrestricted innovation and those favoring regulatory safeguards to address emergent risks. On one side, minimal oversight enables rapid customization of foundation models, driving economic value through specialized applications; for example, platforms like Hugging Face reported over 100,000 fine-tuned models shared by developers in 2024, facilitating advancements in domains from drug discovery to autonomous systems without the resource intensity of full retraining. Industry analyses, such as those from the Cato Institute, warn that excessive rules could mirror historical precedents like early internet regulations, which delayed adoption and ceded leadership to less-constrained jurisdictions.^[84] Critics of overregulation highlight frameworks like the EU AI Act, enacted in 2024, which imposes transparency and risk assessment obligations on general-purpose AI models and their fine-tuned derivatives, potentially classifying many adaptations as high-risk systems requiring conformity evaluations.^[85] This has drawn fire for creating compliance burdens disproportionate to small-scale innovators; a 2025 Center for Data Innovation report estimated that such requirements could increase deployment costs by 20-50% for open-source fine-tuners, favoring incumbents with legal resources while driving development offshore to regions like the US or Asia.^[86] Proponents of deregulation, including figures like Elon Musk, argue that empirical progress in AI—evidenced by fine-tuning's role in achieving state-of-the-art benchmarks on tasks like GLUE scoring 90%+ improvements post-2023—relies on iterative experimentation unhindered by preemptive mandates, which often stem from precautionary biases in academic and regulatory bodies.^[87] ^[88] Conversely, regulatory advocates cite causal evidence from safety research showing fine-tuning's propensity to erode base-model safeguards; a Stanford HAI study in 2024 found that fine-tuning on just 10 adversarial examples disrupted alignment in models like Llama 2, increasing harmful output rates by orders of magnitude.^[79] They contend that without calibrated rules—such as mandatory documentation for systemic-risk models—innovation risks amplifying unmitigated harms, though empirical data on overregulation's stifling effects remains contested, with US fine-tuning startups raising $2.5 billion in funding in 2024 amid lighter federal oversight.^[89] This divide underscores a core tension: while regulations like California's vetoed SB 1047 in 2024 aimed to enforce safety thresholds on frontier models, opponents successfully argued they would preemptively constrain fine-tuning's democratizing potential, preserving a landscape where market incentives, not bureaucratic hurdles, guide responsible advancement.^[90]^[91]

Achievements and Broader Impact

Enhancements in Model Performance

Fine-tuning adapts pre-trained models to downstream tasks, yielding measurable gains in accuracy, efficiency, and task-specific competence by refining parameters on targeted datasets. Supervised fine-tuning (SFT) on small volumes of data—such as 60 question-answering examples—can activate latent pre-trained knowledge in LLMs like LLaMA-2-7B and Qwen-2-7B, boosting overall accuracy on memory-level QA tasks from baseline levels to peaks of 57.42% when using high-memory training data, compared to 47.89% with low-memory inputs. This "diagonal phenomenon" highlights that aligning training data complexity with test demands maximizes performance, with in-domain accuracy reaching 58.38% under optimal conditions. In natural language understanding benchmarks like GLUE, reinforcement learning-based fine-tuning methods, such as PPO applied to transformer models, deliver average score increases of 6.3 points over standard SFT, surpassing models like BERT-large by 2.7 points in some configurations. Instruction tuning, a variant of fine-tuning on (instruction, output) pairs, further elevates generalization; for biomedical tasks, LLMs tuned on datasets like BioInstruct outperform untuned baselines on specialized benchmarks, demonstrating enhanced adherence to domain-specific prompts and reduced errors in output generation.^[92] For open-weight LLMs, fine-tuning smaller variants enables near-proprietary performance: models like LLaMA-3.2, after adaptation, achieve up to 74% accuracy improvements over base versions in multimodal applications, such as vision-language tasks on Amazon Bedrock.^[93] Similarly, fine-tuned LLaMA-2 instances reach ~90% success rates on natural language query processing in enterprise settings, where base models falter due to domain mismatches.^[94] These gains stem from parameter updates that prioritize relevant patterns, though they remain contingent on data quality and task alignment rather than universal scaling.

Economic and Technological Ramifications

Fine-tuning substantially lowers the computational and financial barriers to deploying specialized AI models compared to training from scratch, enabling smaller organizations to participate in AI development. Pre-training large language models (LLMs) often requires thousands of GPUs over weeks or months, incurring costs in the millions of dollars, whereas fine-tuning can be accomplished with a few GPUs in hours or days, typically ranging from $500 to $35,000 depending on model size, data volume, and hardware.^[95]^[96] This efficiency has democratized access, allowing startups and enterprises to customize base models for niche applications without prohibitive infrastructure investments, as evidenced by reports of 90% cost reductions and 300-400% return on investment in the first year for fine-tuned small language models (SLMs).^[97]^[98] Consequently, fine-tuning fosters competition in the AI ecosystem, shifting value from foundational model providers to downstream adapters and reducing reliance on a few dominant players for full-scale training capabilities.^[99] Economically, this paradigm supports broader productivity gains across sectors by facilitating rapid integration of AI into workflows, with generative AI applications—including fine-tuned variants—projected to contribute $2.6 trillion to $4.4 trillion annually to global GDP through enhanced automation and decision-making.^[100] In domains like economics and finance, fine-tuned LLMs have demonstrated improved accuracy on specialized tasks, such as analyzing economic data or generating relevant forecasts, by adapting general-purpose models to domain-specific datasets.^[101] However, this accessibility also introduces market dynamics where optimal pricing strategies for fine-tuned outputs, such as token-based allocation, become critical for providers to balance accessibility with profitability, as analyzed in economic models of LLM deployment.^[102] For smaller firms, fine-tuning SLMs has enabled revenue generation exceeding $47,000 per project by outperforming larger models in targeted use cases while minimizing ongoing inference costs.^[98] Technologically, fine-tuning accelerates innovation by allowing iterative refinement of models for precise tasks, such as instruction-following or domain expertise, without retraining entire architectures, thereby shortening development cycles from months to days.^[7] This has ramifications for scalability, as parameter-efficient techniques like LoRA further reduce resource demands, making high-performance adaptations feasible on consumer-grade hardware and promoting widespread experimentation.^[103] In practice, it enables vertical integrations, such as fine-tuned models for financial analysis that outperform baselines on economics-specific benchmarks after targeted instruction tuning.^[104] Yet, this efficiency can lead to over-reliance on proprietary base models, potentially homogenizing outputs and amplifying vulnerabilities if upstream pre-training flaws propagate through fine-tuning layers. Overall, fine-tuning's technological leverage expands AI's applicability in resource-constrained environments, driving advancements in modular AI systems and hybrid human-AI workflows.^[105]

Future Directions and Emerging Trends

Researchers are advancing parameter-efficient fine-tuning (PEFT) techniques to address the high computational costs of adapting large language models (LLMs), with methods like Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA) enabling updates to only a small fraction of parameters—often less than 1%—while achieving performance comparable to full fine-tuning.^[106] Recent developments include DoRA and LoRA+, which decompose weights into magnitude and direction components for improved stability and generalization, allowing fine-tuning of models up to 65 billion parameters on consumer GPUs.^[107] These approaches reduce memory requirements by up to 90% compared to traditional methods, facilitating deployment on edge devices and democratizing access to customized models.^[108] Continual fine-tuning remains a focus to mitigate catastrophic forgetting, where models lose prior knowledge during sequential adaptation; empirical studies show forgetting rates exceeding 50% in domain-specific tasks without intervention.^[73] Innovations such as CURLoRA and rehearsal-free methods preserve core capabilities by constraining updates to low-rank subspaces or integrating elastic weight consolidation, enabling stable multi-task learning across datasets.^[109]^[110] By 2025, these techniques support lifelong learning paradigms, with evaluations on open-source LLMs under 10 billion parameters demonstrating retention improvements of 20-30% over baseline fine-tuning.^[111] Multimodal fine-tuning is emerging as a dominant trend, extending LLMs to integrate vision, audio, and video modalities for unified reasoning; models like GPT-4o and LLaMA-4 variants are fine-tuned on cross-modal datasets to handle tasks such as image captioning and video analysis with end-to-end training.^[112]^[113] This shift addresses representation shifts during adaptation, where fine-tuning aligns unimodal embeddings into shared spaces, boosting performance on benchmarks like VQA by 15-25% over unimodal baselines.^[113] Trends indicate a move toward synthetic multimodal data generation to scale training without proprietary sources, though risks of model collapse necessitate careful regularization.^[114] Broader directions include domain-adaptive fine-tuning via continued pretraining followed by supervised or preference optimization, as demonstrated in materials science applications where models achieve 10-20% accuracy gains on specialized tasks.^[115] Energy-efficient practices and sparse expertise tuning—focusing updates on task-relevant subnetworks—promise to balance capability enhancements with sustainability, amid projections for multimodal models dominating by 2026.^[116]^[117]

References

[1]
[2110.07783] The Fine-Tuning of the Universe for Life - arXiv
Oct 15, 2021 · When a physicist says that a theory is fine-tuned, they mean that it must make a suspiciously precise assumption in order to explain a certain observation.
[2]
Just Six Numbers: The Deep Forces that Shape the Universe by ...
Jun 8, 2012 · The astronomer royal addresses the cosmic coincidence that six numbers in physics are just right for the emergence of galaxies, stars, chemistry and people.
[3]
Misapprehensions about the Fine-Tuning Argument
Nov 28, 2017 · The fine-tuning argument purports to show that particular aspects of fundamental physics provide evidence for the existence of God.
[4]
Fine-Tuning - Stanford Encyclopedia of Philosophy
Aug 22, 2017 · The argument from fine-tuning for design as reviewed in Section 3.1 treats the fact that life requires fine-tuned conditions as background ...Fine-Tuning and Design · Fine-Tuning and the Multiverse
[5]
The physics of the universe appear to be fine-tuned for life. Why?
May 21, 2025 · The fundamental constants of nature seem perfectly tuned to allow life to exist. If they were even a little bit different, we simply wouldn't be here.
[6]
What is Fine-Tuning? | IBM
Fine-tuning in machine learning is the process of adapting a pre-trained model for specific tasks or use cases. It has become a fundamental deep learning ...Overview · Fine-tuning vs. training
[7]
The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs
Aug 23, 2024 · Transfer Learning: Fine-tuning leverages the knowledge acquired during pre-training, adapting it to specific tasks with reduced computation time ...
[8]
14.2. Fine-Tuning — Dive into Deep Learning 1.0.3 documentation
We set the base learning rate to a small value in order to fine-tune the model parameters obtained via pretraining. Based on the previous settings, we will ...
[9]
Pretraining vs. Fine-tuning: What Are the Differences? - Lightly AI
Pretraining learns fundamental representations self-supervised, while fine-tuning is transfer learning on specialized data to enhance a model for specific ...
[10]
Fine-Tuning vs. Pre-Training: Their Impact on Language Models
Oct 9, 2024 · Pre-training establishes a generalized model while fine-tuning transforms it into a specialized tool tailored to specific needs. For example, an ...
[11]
Analyzing the Relationship between Pre-Training and Fine-Tuning ...
Aug 14, 2024 · In this work, we investigate the relationship between pre-training and fine-tuning by fine-tuning multiple intermediate pre-trained model checkpoints.
[12]
Transfer Learning vs. Model Fine-tuning - Picovoice
Oct 5, 2023 · Transfer learning uses a pre-trained model for similar tasks, while fine-tuning further trains it on a task-specific dataset to improve ...
[13]
Difference Between Fine-Tuning and Transfer Learning
Feb 16, 2024 · Transfer Learning freezes most of the pre-trained model and trains only the final layers, while Fine-Tuning updates part or all of the pre- ...
[14]
Difference between pre-training and fine tuning with language ...
Apr 2, 2025 · Pre-Training: Purpose: Establishes a general understanding of language. · Fine-Tuning: Purpose: Adapts the model to specific tasks or domains.
[15]
Fine-Tuning vs Transfer Learning: Key Differences for ML and LLM ...
Oct 1, 2025 · Transfer learning is often more efficient and works well when data is limited or when the target task is similar to the pre-training domain.
[16]
Reminder of the First Paper on Transfer Learning in Neural ...
This paper describes a work on transfer learning in neural networks carried out in 1970s and early 1980s, which produced its first publication in 1976.
[17]
Reminder of the First Paper on Transfer Learning in Neural ...
It is pointed out that pioneering work on transfer learning took place in early 1990s, and this paper updates that knowledge, pointing out that the research ...
[18]
[PDF] Reminder of the First Paper on Transfer Learning in Neural ...
This paper describes a work on transfer learning in neural networks carried out in 1970s and early. 1980s, which produced its first publication in 1976.
[19]
Neural Networks - History - Stanford Computer Science
MADALINE was the first neural network applied to a real world problem, using an adaptive filter that eliminates echoes on phone lines. While the system is as ...
[20]
[PDF] A Survey on Transfer Learning
In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multi- task ...
[21]
[PDF] ImageNet Classification with Deep Convolutional Neural Networks
... fine-tuning” it on ILSVRC-2012 gives an error rate of. 16.6%. Averaging the predictions of two CNNs that were pre-trained on the entire Fall 2011 re- lease ...Missing: impact | Show results with:impact
[22]
How transferable are features in deep neural networks? - arXiv
In this paper we experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few ...
[23]
A Survey on Deep Transfer Learning and Beyond - MDPI
Oct 3, 2022 · In this survey, we first review more than 50 representative approaches of DTL in the last decade and systematically summarize them into four categories.
[24]
[1810.04805] BERT: Pre-training of Deep Bidirectional Transformers ...
Oct 11, 2018 · As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide ...
[25]
A Decade Survey of Transfer Learning (2010–2020)
This article presents a comprehensive survey on transfer learning, and presents the state of the art, current trends, applications, and open challenges.
[26]
LoRA: Low-Rank Adaptation of Large Language Models - arXiv
Jun 17, 2021 · We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the ...
[27]
[1902.00751] Parameter-Efficient Transfer Learning for NLP - arXiv
Feb 2, 2019 · We propose transfer with adapter modules. Adapter modules yield a compact and extensible model; they add only a few trainable parameters per task.
[28]
Training language models to follow instructions with human feedback
Mar 4, 2022 · In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback.
[29]
Supervised Fine-Tuning (SFT) for LLMs - GeeksforGeeks
Jul 23, 2025 · Supervised Fine-Tuning (SFT) is a process of taking a pre-trained language model and further training them on a smaller, task-specific dataset ...
[30]
Understanding and Using Supervised Fine-Tuning (SFT) for ...
Sep 11, 2023 · Supervised fine-tuning (SFT) is the first training step within the alignment process for LLMs, and it is actually quite simple. First, we need ...
[31]
Instruction Tuning for Large Language Models: A Survey - arXiv
Aug 21, 2023 · This paper surveys research works in the quickly advancing field of instruction tuning (IT), which can also be referred to as supervised fine-tuning (SFT).
[32]
What is supervised fine-tuning? - BlueDot Impact
May 9, 2025 · Supervised fine-tuning (SFT) is one step in the process of aligning AI models with human preferences, by training them on a dataset of examples ...
[33]
Aligning language models to follow instructions - OpenAI
Jan 27, 2022 · We've trained language models that are much better at following user intentions than GPT-3 while also making them more truthful and less toxic.
[34]
Illustrating Reinforcement Learning from Human Feedback (RLHF)
Dec 9, 2022 · That's the idea of Reinforcement Learning from Human Feedback (RLHF); use methods from reinforcement learning to directly optimize a language ...ChatGPT 背后的“功臣” · How-to-train blog post · Proximal Policy Optimization
[35]
How RLHF Preference Model Tuning Works (And How Things May ...
Apr 3, 2023 · In this article, we'll explore how RLHF works, how it truly impacts a language model's behavior, and discuss the current limitations.
[36]
Fine-tune large language models with reinforcement learning ... - AWS
Apr 4, 2025 · The following diagram illustrates reinforcement learning from human feedback (RLHF) compared to reinforcement learning from AI feedback (RLAIF).Fine-Tuning An Llm Using... · Categories Of Human... · Implementation Of An Rlaif...
[37]
Paper Review: Open Problems and Fundamental Limitations of ...
Aug 10, 2023 · RL fine-tuning reduces the diversity of samples produced by a model, leading to “mode collapse”. Studies have found that RLHF fine-tuning ...
[38]
Reinforcement Learning From Human Feedback (RLHF) For LLMs
Reinforcement Learning from Human Feedback (RLHF) has turned out to be the key to unlocking the full potential of today's large language models (LLMs).
[39]
[2403.14608] Parameter-Efficient Fine-Tuning for Large Models - arXiv
Mar 21, 2024 · In this survey, we present comprehensive studies of various PEFT algorithms, examining their performance and computational overhead.
[40]
[2410.19878] Parameter-Efficient Fine-Tuning in Large Models - arXiv
Oct 24, 2024 · Abstract page for arXiv paper 2410.19878: Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies.
[41]
Prefix-Tuning: Optimizing Continuous Prompts for Generation - arXiv
Jan 1, 2021 · In this paper, we propose prefix-tuning, a lightweight alternative to fine-tuning for natural language generation tasks, which keeps language model parameters ...
[42]
The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs
Aug 23, 2024 · Abstract:This report examines the fine-tuning of Large Language Models (LLMs), integrating theoretical insights with practical applications.
[43]
Natural language processing with transformers: a review - PMC - NIH
Aug 7, 2024 · In the fine-tuning stage, they added a linear classification layer to predict named entities using labeled clinical concepts from the training ...
[44]
Fine-Tuning LLMs: A Guide With Examples - DataCamp
Learn how fine-tuning large language models (LLMs) improves their performance in tasks like language translation, sentiment analysis, and text generation.
[45]
BERT applications in natural language processing: a review
Mar 15, 2025 · The BERT model has made a substantial impact in the advancement of an extensive range of conventional and advanced NLP tasks. Table 2 presents ...
[46]
[2207.14381] Pro-tuning: Unified Prompt Tuning for Vision Tasks
Jul 28, 2022 · In computer vision, fine-tuning is the de-facto approach to leverage pre-trained vision models to perform downstream tasks. However, deploying ...
[47]
[2211.09359] How to Fine-Tune Vision Models with SGD - arXiv
Nov 17, 2022 · SGD and AdamW are the two most used optimizers for fine-tuning large neural networks in computer vision. When the two methods perform the same, ...
[48]
Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models - arXiv
Jun 29, 2025 · They can be categorized into three main tasks: image recognition (31 datasets), video recognition (7 datasets), and dense prediction (8 datasets) ...
[49]
[2311.15010] Adapter is All You Need for Tuning Visual Tasks - arXiv
Nov 25, 2023 · Pre-training & fine-tuning can enhance the transferring efficiency and performance in visual tasks. Recent delta-tuning methods provide more ...
[50]
[2302.08242] Tuning computer vision models with task rewards - arXiv
Feb 16, 2023 · We adopt this approach and show its surprising effectiveness across multiple computer vision tasks, such as object detection, panoptic segmentation, ...
[51]
Fine-Tuning a Vision Language Model (Qwen2-VL-7B) with the ...
In this recipe, we'll demonstrate how to fine-tune a Vision Language Model (VLM) using the Hugging Face ecosystem, specifically with the Transformer ...
[52]
How to Fine-Tune Multimodal Models or VLMs with Hugging Face TRL
Sep 30, 2024 · Learn how to fine-tune multimodal models like Llama 3.2 Vision or Qwen 2 VL to create custom image-to-text generation models.
[53]
Fine-Tuning Large Vision-Language Models as Decision-Making ...
May 16, 2024 · We propose an algorithmic framework that fine-tunes VLMs with reinforcement learning (RL). Specifically, our framework provides a task description and then ...
[54]
vision language models finetuning notebooks & use cases ... - GitHub
This project walks you through fine-tuning MedGemma 4B, Google's powerful multimodal model optimized for medical applications. MedGemma combines a SigLIP vision ...Fine-Tuning Vision-Language... · Key Features · Example1 Florence2...
[55]
An Empirical Study on Parameter-Efficient Fine-Tuning for ... - arXiv
Jun 7, 2024 · This paper conducts empirical studies using four popular PEFT methods to fine-tune the LLM component of open-source MLLMs.
[56]
Fine-tune multimodal models for vision and text use cases on ... - AWS
Nov 15, 2024 · In this post, we showcase how to fine-tune a text and vision model, such as Meta Llama 3.2, to better perform at visual question answering tasks.
[57]
A Survey on Large Language Models for Critical Societal Domains
This survey paper summarizes the state of domain-specific LLMs in finance, medicine, and law, draws shared connections across these settings for ethnical ...<|separator|>
[58]
Fine-Tuning Large Language Models for Specialized Use Cases - NIH
In this review, we outline some of the major methodologic approaches and techniques that can be used to fine-tune LLMs for specialized use cases.Missing: 2020s | Show results with:2020s<|separator|>
[59]
Fine-tuning medical language models for enhanced long-contextual ...
Jun 3, 2025 · This study aims to investigate the problem of the decline in performance of Med-LLMs in long-context understanding.
[60]
Customizing models for legal professionals | OpenAI
Harvey partnered with OpenAI to create a custom-trained case law model. This has allowed Harvey to deliver AI systems that help with tasks requiring complex ...
[61]
BigLaw Bench – Retrieval - Harvey AI
Nov 13, 2024 · Harvey's retrieval system outperforms commonly used embedding-based and reranking methods, identifying up to 30% more relevant content than alternative ...
[62]
Understanding the Effects of Domain Finetuning on LLMs - arXiv
Oct 10, 2025 · 1. Improving performance on domain-specific benchmarks: fine-tuning enhances an LLM's performance on specialised benchmarks, particularly in ...
[63]
Memory requirements for fine-tuning Llama 2 - Medium
Apr 15, 2024 · Naively fine-tuning Llama-2 7B takes 110GB of RAM! ... Even fine-tuning small models like Llama-2 7B on regular consumer GPUs can be challenging ...Naively fine-tuning Llama-2 7B... · Low Rank Adaptation (LoRA...
[64]
Llama 2: Efficient Fine-tuning Using Low-Rank Adaptation (LoRA ...
However, the Llama 2 model is resource-intensive, requiring a minimum of four NVIDIA GPUs. Options for future work are to explore smaller models and compare the ...<|control11|><|separator|>
[65]
LLMs performance bottleneck: memory bandwidth not capacity
Oct 3, 2025 · Here's what's happening: the bottleneck isn't memory capacity (GB available), it's memory bandwidth (GB/s transferred per second). At low batch ...<|separator|>
[66]
[D] Why is GPU utilization so bad when training neural networks?
Dec 5, 2020 · Network training has low FLOP utilization because some other aspect of the system is already being fully utilized eg GPU memory bandwidth is ...[D] What is the motivation for parameter-efficient fine tuning if there's ...[D] Estimating hardware for finetuning LLM : r/MachineLearningMore results from www.reddit.comMissing: LLMs | Show results with:LLMs
[67]
Isnt finetuning extremely expensive in the cloud? : r/LocalLLaMA
Sep 3, 2024 · Running your fine tuned model on API services is very expensive because it means you essentially need your own hardware reservation (while using ...<|separator|>
[68]
LLM Fine-Tuning on a Budget: Top FAQs on Adapters, LoRA, and ...
Aug 28, 2025 · Parameter-efficient fine-tuning (PEFT) adapts LLMs by training tiny modules—adapters, LoRA, prefix tuning, IA³—instead of all weights, ...Missing: 2020s | Show results with:2020s
[69]
Why Parameter Efficient Fine Tuning is always preferred over full ...
Full fine-tuning requires updating billions of parameters, demanding high-end GPUs and more memory, whereas PEFT ...
[70]
Parameter Efficient Instruction Tuning: An Empirical Study - arXiv
Nov 25, 2024 · ... full parameter finetuning is overwhelmingly costly. Therefore, Parameter Efficient Finetuning (PEFT) has arisen as a cost-effective practice ...
[71]
The Impact of Fine-tuning Large Language Models on Automated ...
Jul 26, 2025 · We observe that full fine-tuning techniques decrease the benchmarking performance of various models due to different data distributions and ...
[72]
Quantifying and Mitigating Prompt Overfitting - arXiv
Oct 29, 2024 · In this paper, we show that LLMs fine-tuned with reinforcement learning tend to overfit to the specific prompts they have been trained on, and propose ...<|separator|>
[73]
[2308.08747] An Empirical Study of Catastrophic Forgetting in Large ...
Aug 17, 2023 · The experiments reveal that catastrophic forgetting is generally observed in LLMs ranging from 1b to 7b parameters. Surprisingly, as the model ...
[74]
[PDF] Revisiting Catastrophic Forgetting in Large Language Model Tuning
Nov 12, 2024 · Catastrophic Forgetting (CF) means LLMs forget prior knowledge when learning new data, compromising their effectiveness during fine-tuning.
[75]
[PDF] Unveiling the Generalization Power of Fine-Tuned Large Language ...
Jun 16, 2024 · Fine-tuned LLMs show different generalization behaviors; classification tasks transfer positively, while generation tasks often experience ...
[76]
A comprehensive survey of Vision–Language Models: Pretrained ...
It includes issues such as overfitting during fine-tuning, prompt sensitivity in few-shot scenarios, scalability constraints of adapters, and biases or ...
[77]
Generalization Challenges in Instruction-Tuned LLMs for Spatial ...
May 23, 2025 · Our results reveal that while models generalize well on simple tasks, their performance degrades significantly on more complex tasks. We present ...
[78]
Fine-tuning Aligned Language Models Compromises Safety, Even ...
Oct 5, 2023 · Our red teaming studies find that the safety alignment of LLMs can be compromised by fine-tuning with only a few adversarially designed training examples.
[79]
Safety Risks from Customizing Foundation Models via Fine-Tuning
Jan 8, 2024 · We find that access to fine-tuning can easily disrupt safety mechanisms: Fine-tuning on just 10 harmful data points with very little cost caused ...
[80]
Fine-Tuning LLMs Breaks Their Safety and Security Alignment
May 28, 2024 · Fine-tuning large language models can compromise their safety and security, making them more vulnerable to jailbreaks and harmful outputs.
[81]
Fine-Tuning LLMs Breaks Their Safety and Security Alignment
May 28, 2024 · We found fine-tuned variants more than 3 times more susceptible to jailbreak instructions and over 22 times more likely to produce a harmful response than the ...
[82]
Why LLM Safety Guardrails Collapse After Fine-tuning - arXiv
Jun 5, 2025 · Title:Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets · Submission history.
[83]
An Empirical Study on Safety Alignment after Instruction Tuning - arXiv
Feb 3, 2025 · In this study, we systematically examine the factors contributing to safety alignment degradation in benign fine-tuning scenarios.
[84]
Why AI Overregulation Could Kill the World's Next Tech Revolution
Sep 3, 2025 · Overreach of government regulation can pose a grave threat to nascent, promising technologies. This is particularly true in the case of AI, with ...
[85]
General-Purpose AI Models in the AI Act – Questions & Answers
Jul 10, 2025 · General-purpose AI models may be further modified or fine-tuned into new models (recital 97 AI Act). Accordingly, downstream entities that fine- ...
[86]
The EU's AI Act Creates Regulatory Complexity for Open-Source AI
Mar 4, 2024 · Combined with the law's broad scope, the AI Act will significantly impact the development and use of open-source AI in the EU. The AI Act ...
[87]
Elon Musk, OpenAI, Anthropic differ on California's AI safety bill - Axios
Aug 28, 2024 · A California effort to regulate AI has divided the tech world, with some trying to squelch what they see as overreach by a single state and others supporting ...
[88]
Balancing market innovation incentives and regulation in AI
Sep 24, 2024 · Central to this debate are two implicit assumptions: that regulation rather than market forces primarily drive innovation outcomes and that AI ...<|separator|>
[89]
Balancing the tradeoff between regulation and innovation for ...
We developed an economic theory on how the welfare-maximizing level of regulatory stringency for AI depends on various institutional parameters.
[90]
California AI bill divides Silicon Valley, draws in national policymakers
Aug 28, 2024 · Major technology firms, AI startups and researchers are split over whether the legislation would stifle innovation on the rapidly developing ...
[91]
AI Regulation vs Innovation: Global Sector Leaders Weigh in
Jun 19, 2025 · Salesforce and Heathrow leaders argue that AI regulations improve trust and adoption, while XPRIZE warns that over-regulation drives innovation offshore.
[92]
BioInstruct: instruction tuning of large language models for ...
Jun 4, 2024 · We find that LLMs fine-tuned on BioInstruct significantly improve performance on the benchmark compared to competitive baselines. We further ...
[93]
Best practices for Meta Llama 3.2 multimodal fine-tuning on Amazon ...
May 1, 2025 · Our experiments show that fine-tuned Meta Llama 3.2 models can achieve up to 74% improvements in accuracy scores compared to their base versions ...
[94]
Fine-Tuning Llama-2: Tailoring Models to Unique Applications
Aug 11, 2023 · Dark colors present chat model performance. Fine-tuned models achieve ~90% success rate. Note that some of the natural language queries in that ...
[95]
Understanding the Performance and Estimating the Cost of LLM ...
Aug 8, 2024 · Another attractive feature of fine-tuning LLMs is that it can be achieved at a cost-efficient manner. While pre-training LLMs require thousands ...
[96]
What is the cost of fine-tuning LLMs? | by The Educative Team
Jul 1, 2025 · Final thoughts. Fine-tuning LLMs can cost as little as $500 or more than $35,000. The difference depends on your architecture choices, data ...
[97]
Understanding Fine-Tuning in AI and ML | Databricks
Even small companies can build customized models suited to their needs and budgets. Fine-tuning significantly reduces the need to invest in costly ...
[98]
The $47K Fine-Tuning Revolution: How Small Language Models ...
Jul 26, 2025 · But here's what nobody talks about: fine-tuning your own SLM can deliver 300-400% ROI in the first year while cutting costs by 90%. Today, I'm ...
[99]
https://today.ucsd.edu/story/ai-models-can-now-be-customized-with-far-less-data-and-computing-power
[100]
Economic potential of generative AI - McKinsey
Jun 14, 2023 · Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we ...<|separator|>
[101]
Large language models: a primer for economists
Dec 10, 2024 · This process, known as fine-tuning, adjusts the LLM to the specific economic data and research questions, yielding more accurate and relevant ...
[102]
Token Allocation, Fine-Tuning, and Optimal Pricing - arXiv
Feb 11, 2025 · Abstract:We develop an economic framework to analyze the optimal pricing and product design of Large Language Models (LLM).
[103]
Fine-tuning methods for LLMs: A comparative guide - Outshift | Cisco
Aug 27, 2024 · While fine-tuning is more efficient and cost-effective than training a model from scratch, not all methodologies are created equal. Because ...
[104]
[PDF] EconNLI: Evaluating Large Language Models on Economics ...
Aug 11, 2024 · The open-source model with the best performance is FINMA, indicating that tuning on financial instructions improves the model's capability in ...
[105]
Towards Integrated Fine-tuning and Inference when Generative AI ...
Jan 5, 2024 · 2) Fine-tuning is the re-optimization of the GAI model after pre-training. The fine-tuning for different vertical domains makes the original ...Missing: ramifications | Show results with:ramifications
[106]
Advances in Parameter-Efficient Fine-Tuning - Preprints.org
Mar 27, 2025 · This survey provides a comprehensive review of PEFT techniques, categorizing existing approaches into adapter-based tuning, low-rank adaptation (LoRA), prefix ...
[107]
Advanced LLM Fine-Tuning: LoRa, QLora, Dora & Lora+
Oct 12, 2025 · Our guide explores parameter-efficient fine-tuning (PEFT), from the core principles of LoRA to advanced techniques like QLoRA, DoRA, ...
[108]
https://deepfa.ir/en/blog/qlora-quantized-low-rank-adaptation-llm-fine-tuning
[109]
CURLoRA: Stable LLM Continual Fine-Tuning and Catastrophic ...
Our method addresses two critical challenges in LLM fine-tuning: mitigating catastrophic forgetting during continual learning and reducing the number of ...
[110]
[PDF] Mitigating Catastrophic Forgetting in Large Language Models with ...
Aug 11, 2024 · Large language models (LLMs) suffer from catastrophic forgetting during continual learn- ing. Conventional rehearsal-based methods rely on ...
[111]
Catastrophic Forgetting in LLMs: A Comparative Analysis Across ...
Apr 1, 2025 · This study evaluates the continual fine-tuning of various open-source LLMs with different parameter sizes (specifically models under 10 billion parameters) on ...
[112]
6 Best Multimodal AI Models in 2025 - Times Of AI
Aug 22, 2025 · Top Multimodal AI Models in 2025 · GPT-4o by OpenAI · Gemini 2.5 Flash & Pro · Claude 3.7 (Anthropic) · Grok-4 Multimodal (xAI/Elon Musk) · LLaMA-4 ...
[113]
Analyzing Fine-tuning Representation Shift for Multimodal LLMs ...
Jan 6, 2025 · Our work sheds light on how multimodal representations evolve through fine-tuning and offers a new perspective for interpreting model adaptation ...
[114]
Multimodal Synthetic Data Finetuning and Model Collapse
Oct 12, 2025 · Our findings provide initial insights and practical guidelines for reducing the risk of model collapse in self-improving multi-agent AI systems ...
[115]
Fine-tuning large language models for domain adaptation - Nature
Mar 28, 2025 · In this work, we explore the effects of Continued Pretraining (CPT), Supervised Fine-Tuning (SFT), and various preference-based optimization approaches.
[116]
The Future of Large Language Models - Research AIMultiple
Oct 10, 2025 · Future trends of large language models · 1- Fact-checking with real-time data integration · 2- Synthetic training data · 3- Sparse expertise · 4- ...
[117]
Integrative innovation of large language models in industries
Jul 2, 2025 · Future research directions include achieving a balance between enhancing model capabilities and managing energy consumption, as well as ...