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On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Supervised Fine-TuningLarge Language ModelReinforcement Learning
Abstract:

In this work, we present a simple yet theoretically motivated improvement to Supervised Fine-Tuning (SFT) for the Large Language Model (LLM), addressing its limited generalization compared to reinforcement learning (RL). Through mathematical analysis, we reveal that standard SFT gradients implicitly encode a problematic reward structure that may severely restrict the generalization capabilities of model compared to RL. To rectify this, we propose Dynamic Fine-Tuning (DFT), stabilizing gradient updates for each token by dynamically rescaling the objective function with the probability of this token. With just a single-line change, the method outperforms standard SFT on multiple difficult benchmarks and base models, from math reasoning to code generation and multi-modal tasks, demonstrating improved generalization. Additionally, DFT achieves competitive results in offline RL settings, and further boosts the effectiveness of subsequent RL training, providing an effective yet streamlined alternative. The experiments further demonstrate that DFT not only strengthens SFT performance but also consistently improves the effectiveness of subsequent RL training. By bridging theoretical insights with practical solutions, this work advances the state of SFT. The source code will be publicly released.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes Dynamic Fine-Tuning (DFT), which rescales the SFT objective by token probability to stabilize gradients and improve generalization. It sits within the 'Gradient and Token-Level Weighting' leaf of the taxonomy, which contains only three papers total. This is a relatively sparse research direction compared to more crowded areas like 'Alternative Loss Formulations' (four papers) or 'Single-Domain Applications' (seven papers). The small number of sibling papers suggests this specific approach to gradient stabilization through dynamic rescaling is not yet heavily explored, though the broader category of training objective modifications is well-represented across the taxonomy.

The taxonomy reveals several neighboring research directions that address generalization through different mechanisms. The sibling leaf 'Alternative Loss Formulations' explores contrastive learning and game-theoretic objectives, while 'Regularization Techniques' adds constraints to prevent overfitting. Adjacent branches include 'Data Selection and Optimization', which tackles generalization through data curation rather than objective modification, and 'Multi-Stage Training and SFT-RL Integration', which combines supervised and reinforcement learning phases. The paper's focus on gradient-level intervention distinguishes it from these data-centric or multi-stage approaches, though the theoretical connection to RL (via implicit reward analysis) bridges these categories conceptually.

Among the 27 candidates examined, the contribution-level analysis reveals varying degrees of prior overlap. The mathematical equivalence between SFT and policy gradient (Contribution 1) examined 10 candidates with 5 appearing refutable, suggesting this theoretical insight has substantial precedent in the limited search scope. The DFT method itself (Contribution 2) examined 9 candidates with only 2 refutable, indicating the specific rescaling technique may be more novel. The theoretical framework connecting SFT limitations to reward structure (Contribution 3) examined 8 candidates with 1 refutable. These statistics reflect a top-K semantic search, not an exhaustive review, so the true novelty landscape may differ with broader coverage.

Based on the limited search scope of 27 candidates, the work appears to occupy a moderately explored niche. The gradient weighting direction is sparse within the taxonomy, but the theoretical SFT-RL connection has more precedent among examined papers. The single-line implementation simplicity contrasts with more complex multi-stage or data-centric methods, though whether this simplicity translates to practical adoption remains an empirical question. The analysis covers top semantic matches and does not account for concurrent work or domain-specific applications outside the search scope.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
27
Contribution Candidate Papers Compared
8
Refutable Paper

Research Landscape Overview

Core task: improving generalization of supervised fine-tuning for large language models. The field is organized around several complementary strategies. Training Objective and Loss Function Modifications explore novel loss designs and gradient weighting schemes to prioritize informative tokens or samples. Data Selection and Optimization focus on curating high-quality training sets through mixing strategies, diversity preservation, and semi-supervised approaches. Parameter-Efficient and Resource-Constrained Fine-Tuning develop methods like LoRA[10] and quantization-aware techniques to reduce computational overhead while maintaining performance. Domain-Specific and Task-Specific Adaptation address specialized applications ranging from healthcare[11] to agent-based systems[30], while Multi-Stage Training and SFT-RL Integration examine how supervised fine-tuning interacts with reinforcement learning phases[8][9]. Theoretical Analysis and Empirical Studies provide foundational insights into generalization mechanisms[6], and Alternative Fine-Tuning Paradigms propose entirely new training frameworks beyond standard supervised learning. A particularly active line of work centers on token-level and gradient-based weighting, where methods aim to identify and emphasize the most critical parts of training data. Reward Rectification[0] fits within this branch by leveraging reward signals to adjust token-level contributions during fine-tuning, addressing the challenge of noisy or uninformative tokens that can hinder generalization. This approach contrasts with Token Cleaning[32], which removes problematic tokens entirely, and Critical Token Fine-Tuning[47], which explicitly identifies and prioritizes high-impact tokens. These methods share the intuition that not all tokens contribute equally to learning, but differ in whether they filter, reweight, or selectively emphasize. The trade-off between computational overhead and generalization gains remains an open question, as does the interplay between token-level interventions and broader data selection strategies like those in Data Mixing Optimization[2] or Preserving Diversity[3].

Claimed Contributions

Mathematical equivalence between SFT and policy gradient with implicit reward

The authors mathematically establish that the SFT gradient can be rewritten as a policy gradient with an implicit sparse reward inversely proportional to the model's probability of expert actions. This formulation reveals why SFT exhibits limited generalization compared to RL methods.

10 retrieved papers
Can Refute
Dynamic Fine-Tuning (DFT) method

The authors introduce DFT, a simple modification to SFT that rescales the objective function at each token by its probability. This one-line change neutralizes the inverse-probability weighting distortion, resulting in more stable gradients and improved generalization across multiple tasks.

9 retrieved papers
Can Refute
Theoretical framework connecting SFT limitations to reward structure

The authors provide a theoretical analysis showing that SFT's gradient can be interpreted as an on-policy gradient method with a sparse indicator reward biased by importance weighting. This framework explains SFT's instability and limited generalization, motivating the proposed correction.

8 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Mathematical equivalence between SFT and policy gradient with implicit reward

The authors mathematically establish that the SFT gradient can be rewritten as a policy gradient with an implicit sparse reward inversely proportional to the model's probability of expert actions. This formulation reveals why SFT exhibits limited generalization compared to RL methods.

Contribution

Dynamic Fine-Tuning (DFT) method

The authors introduce DFT, a simple modification to SFT that rescales the objective function at each token by its probability. This one-line change neutralizes the inverse-probability weighting distortion, resulting in more stable gradients and improved generalization across multiple tasks.

Contribution

Theoretical framework connecting SFT limitations to reward structure

The authors provide a theoretical analysis showing that SFT's gradient can be interpreted as an on-policy gradient method with a sparse indicator reward biased by importance weighting. This framework explains SFT's instability and limited generalization, motivating the proposed correction.

Table of Contents