Enterprise AI Analysis
GFT: From Imitation to Reward Fine-Tuning with Unbiased Group Advantages and Dynamic Coefficient Rectification
This paper introduces Group Fine-Tuning (GFT), a novel post-training framework for large language models. GFT addresses limitations of traditional supervised fine-tuning (SFT) like single-path dependency and gradient explosion. It achieves this through two mechanisms: Group Advantage Learning (GAL), which uses diverse response groups and normalized contrastive supervision, and Dynamic Coefficient Rectification (DCR), which adaptively bounds inverse-probability weights. Experiments show GFT outperforms SFT-based methods, integrates better with subsequent RL, and mitigates catastrophic forgetting, offering a more stable and generalizable post-training paradigm.
Executive Impact
Key Performance Indicators & Strategic Advantages
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Performance Improvement
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Catastrophic Forgetting Reduction
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Exploration Diversity
Deep Analysis & Enterprise Applications
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SFT Limitations
GFT Mechanisms
Experimental Results
Explores the inherent weaknesses of Supervised Fine-Tuning, including single-path dependency and gradient instability, which hinder model generalization.
Details Group Advantage Learning (GAL) and Dynamic Coefficient Rectification (DCR) as core components addressing SFT's shortcomings.
Summarizes the empirical findings, demonstrating GFT's superior performance, data efficiency, and compatibility with RL.
20.66%
Average Accuracy Increase over SFT on challenging Math Benchmarks
Enterprise Process Flow
Supervised Fine-Tuning (SFT)
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Single-Path Dependency
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Gradient Explosion
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Group Fine-Tuning (GFT)
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Group Advantage Learning (GAL)
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Dynamic Coefficient Rectification (DCR)
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Improved Generalization & Stability
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GFT's Impact on Math Reasoning with Qwen2.5-Math-1.5B
Problem: Traditional SFT on Qwen2.5-Math-1.5B showed performance degradation and catastrophic forgetting on challenging math benchmarks like Gaokao2023En and Minerva Math, limiting its real-world applicability for complex reasoning tasks.
Solution: Applying GFT, with its Group Advantage Learning and Dynamic Coefficient Rectification, to Qwen2.5-Math-1.5B allowed the model to learn from diverse response groups (expert, teacher, self-generated) and stabilized the training process against gradient explosions. This enabled more robust knowledge injection and preservation of general-purpose reasoning.
Results: GFT consistently outperformed SFT and other baselines, achieving significant accuracy improvements (e.g., +15.93% on AMC23, +23.51% on Gaokao2023En). It demonstrated stronger data efficiency, better integration with subsequent RL training, and markedly mitigated catastrophic forgetting compared to SFT, leading to a more capable and stable model for complex math reasoning.
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