Fine-Tuning Method Overview

Updated 7 February 2026

Fine-tuning method is the process of adapting pre-trained models to new tasks by selectively updating parameters with task-specific data.
It includes full and parameter-efficient approaches like LoRA, adapters, and prompt tuning to optimize resource use and performance.
Empirical studies show that fine-tuning enhances accuracy, mitigates overfitting, and improves robustness across modalities.

Fine-tuning Method

Fine-tuning refers to the process of adapting a pre-trained model to a new, often downstream, task. This is achieved by further training the model—either all or a carefully selected subset of its parameters—using task-specific data. Fine-tuning is foundational across modalities, including NLP, vision, control, simulators, and physical modeling. The landscape of fine-tuning spans full-parameter updates, highly parameter-efficient approaches, reinforcement learning integration, robust and theoretically justified regularization schemes, domain-specific architectural modifications, and precise definitions and quantifications in scientific modeling.

1. Core Fine-Tuning Paradigms

Full Fine-Tuning updates all model parameters to adapt to the new task. This remains the baseline for transfer learning, especially when abundant downstream data and compute are available. However, it becomes infeasible in large models due to computational demand and storage constraints, particularly in LLMs and deep vision models.

Parameter-Efficient Fine-Tuning (PEFT) focuses on updating a small, carefully selected subset of parameters, often amounting to less than 1% of the full model, or using structured re-parameterizations. Key PEFT schemes include:

Adapters / Low-Rank Updates (LoRA): Insert small trainable layers or apply low-rank perturbations to the pre-trained weights (Fu et al., 2022, Wang et al., 2023, Kumar et al., 12 Dec 2025).
Prompt-based adaptation: Introduce learnable prompt inputs that steer the frozen backbone toward the new task (Ye et al., 19 Jan 2026).
Selective Sparse Fine-Tuning: Identify the most critical parameters to update via criteria such as empirical Fisher information, gradient norm, or second-order approximations (Fu et al., 2022, Dong et al., 2024, Kumar et al., 12 Dec 2025, Chekalina et al., 2024).

Sequential and Joint Fine-Tuning: Fine-tuning can proceed in multiple optimization phases, such as prefix-tuning followed by LoRA, or by composing prompts with adapters (Wang et al., 2023).

Domain- or Task-Specific Modifications: In reinforcement learning, online and offline fine-tuning may be unified with consistency-based objectives and safe human-in-the-loop intervention (Chen et al., 8 Feb 2025), while in physical systems, gradient-free global optimization with constrained search (e.g., CMA-ES) is used to fine-tune mechanistic model parameters (Suyama et al., 2023).

2. Parameter Efficiency, Selection, and Regularization

Parameter-efficient strategies are motivated by efficiency, stability, and generalization:

Mask-based and Low-rank Methods: Techniques like LoRA and selective sparse masks identify the “delta” parameters most impactful for transfer, based on gradient, Fisher information, or quadratic approximation (Fu et al., 2022, Dong et al., 2024, Kumar et al., 12 Dec 2025).
Second-Order Masking (SAM): Formally selects parameters to maximize predicted impact on loss reduction, based on closed-form solutions to a quadratic expansion (Fu et al., 2022).
AdaGradSelect: For small LLMs, dynamically samples layers to train as a function of their gradient norms, using Dirichlet and ε-greedy strategies, converging on the most critical blocks (Kumar et al., 12 Dec 2025).
SparseGrad: Identifies and updates a sparse basis in MLP gradient space, reducing update count to ≈1% of layer elements while retaining or improving task accuracy, particularly in transformer MLP blocks (Chekalina et al., 2024).

Sparsity can be seen as imposing a strong L2-regularizer toward the pre-trained representation, which improves stability and generalization as formalized by tight bounds on hypothesis stability under parameter sparsity (Fu et al., 2022, Tian et al., 2023).

3. Fine-Tuning Workflows, Losses, and Algorithms

Workflow stages typically include:

Pre-training: Backbone model trained on broad tasks or data, often with unsupervised or self-supervised objectives.
Prompt/Adapter/PEFT Injection: Insert, initialize, or identify new trainable parameters or layers—prompts, adapter weights, low-rank matrices, masks.
Fine-Tuning: Actual adaptation using labeled downstream data, now only updating the selected or inserted parameters. Losses may include standard cross-entropy for classification/QA/generation, contrastive or reconstructive losses (self-supervised, representational), consistency losses for RL, or explicit quadratic regularizers.

Objective Function Design:

Supervised loss: Cross-entropy, MSE, likelihood, or custom task loss.
Unsupervised/self-supervised penalties: BYOL-style contrastive (Ye et al., 19 Jan 2026), mutual-information for rare concepts (Yang et al., 2023), or reconstruction MSE for flow models (Li et al., 2 Oct 2025).
Regularizers: Trust-region constraints (parametric or adaptive projections per layer), KL between perturbed and original outputs (R3F/R4F (Aghajanyan et al., 2020)), information bottleneck, or domain-informed physics constraints (Tian et al., 2023, Li et al., 2 Oct 2025).

Pipeline variants:

Mini-batch (Breadth-First) vs. Sample-wise (Depth-First): Breadth-first mini-batch pipelines with multi-epoch optimization (standard in ML but often ignored in model editing of LLMs) are essential for stability and to prevent catastrophic overwrite, as demonstrated by LocFT-BF (Yang et al., 26 Sep 2025).
Adaptive Layer or Parameter Selection: Adaptive selection based on per-layer or per-parameter statistics (e.g., gradient norms, Fisher information), adjusting the subset of updated parameters or blocks dynamically (Kumar et al., 12 Dec 2025, Dong et al., 2024).
Fine-tuning under Distribution Shift: If the true downstream instance is out-of-distribution relative to pretraining, focus fine-tuning in a perturbative neighborhood of the current best guess, simulate synthetic data, and re-train locally (Lakshminarayanan et al., 6 Apr 2025).

4. Empirical Benchmarks and Generalization

Fine-tuning approaches are evaluated extensively on transfer datasets across language (GLUE, MetaMathQA, GSM8K, etc.), vision (ImageNet, VTAB-1K, CUB-200, FGVC Aircraft, etc.), control (robotic manipulation, digital twins), and structured data (Text2SQL, database question answering). Metrics include:

Classification/generation accuracy, F1, BLEU/ROUGE scores, execution accuracy.
Resource efficiency: Number of trainable parameters, GPU memory, wall-clock time.
Robustness: Out-of-distribution accuracy, resistance to representational collapse (Aghajanyan et al., 2020), negative transfer (Yang et al., 2023), catastrophic forgetting (Yang et al., 26 Sep 2025).
Scalability: Ability to sustain correctness and capability over tens to hundreds of thousands of intervention edits (in LLM editing), or when applied to models with 72B+ parameters (Yang et al., 26 Sep 2025).
Practical effect: For instance, graph prompt fine-tuning achieves F1=91.3% on WSN anomaly detection with only prompt updates (Ye et al., 19 Jan 2026); AdaGradSelect on SLMs achieves full-finetune-level accuracy with 35% less memory (Kumar et al., 12 Dec 2025); and IRD achieves substantial GLUE gains (e.g., +8pp QQP, +5.7pp CoLA vs. mask baselines at 0.5% parameter updates) (Dong et al., 2024).

5. Domain-Specific Extensions and Theoretical Insights

Reinforcement Learning and Control: Fine-tuning architectures incorporate two-stage pipelines (offline behavior cloning/Q-learning and online RL), unified objectives, and safety via human-in-the-loop augmentation for contact-rich robotic tasks (Chen et al., 8 Feb 2025).

Symbolic/Rule-Driven Fine-Tuning: In adaptive deep belief networks, knowledge acquisition comprises identifying misclassified paths, extracting symbolic rules, and gating neurons accordingly at inference without weight updates, significantly increasing test accuracy (Kamada et al., 2018).

Physical Sciences and Gauge Theory: Fine-tuning extends to model calibration (digital twins, MMG models), reducing OOD error via constrained synthetic data generation and direct optimization, as well as providing rigorous, quantitative measures of fine-tuning—local (differential) and global (range-based) metrics—for sensitivity and allowed parameter volume in physical models (Azhar et al., 2018).

Causal and Mutual Information Objectives: Mitigation of negative transfer in vision is achieved by maximizing the mutual information of rare spatial patches and introducing front-door adjustment (channel- and patch-level attention) to deconfound predictions (Yang et al., 2023).

6. Calibration and Post-Hoc Adjustments

Post-hoc calibration can recover accuracy for classes absent during fine-tuning. A single bias (estimated by average logit gap or pseudo-cross-validation) added to the logits of held-out classes almost completely restores their classification accuracy, exposing that catastrophic forgetting is primarily due to logit scale mismatch, not loss of feature separability or class geometry (Mai et al., 2024).

Best Practices: Always measure NCM-based discrimination on held-out classes after fine-tuning; apply a one-dimensional logit shift to recover performance on unobserved classes.

7. Limitations, Open Challenges, and Future Directions

Hyperparameter Sensitivity: Many fine-tuning approaches rely on careful selection of sparsity rates, learning rates, and prompt/adaptor dimensions.
Overfitting Risk: PEFT and prompt-tuning can overfit small datasets; stability mechanisms or outer-loop validation are critical.
Scalability: Computational overhead for mask selection, gradient collection, or SVDs (e.g., in SparseGrad) can become significant for very large models or tasks (Chekalina et al., 2024, Dong et al., 2024).
Domain Transfer: Methods validated in one modality may not transfer directly; for instance, prefix+LoRA efficacy depends on both model and task domain (Wang et al., 2023).
Theory/Practice Gaps: Theoretical explanations for generalization and stability (e.g., regularization via sparsity, contraction in ODE flows) are rapidly advancing but still lag empirical adoption (Fu et al., 2022, Li et al., 2 Oct 2025).
Fine-tuning over Structure: Especially in LLM editing or vision transformers, optimal location and granularity of parameter updates remain active areas, with success hinging on precise architectural targeting (e.g., MLP_down of later layers for editing) (Yang et al., 26 Sep 2025, Chen, 2023).