Title: RL Fine-Tuning Heals OOD Forgetting in SFT
Primary area: foundation or frontier models, including LLMs
Keywords: Reinforcement Learning Fine-tuning, Supervised Fine-tuning, OOD Forgetting, Two-stage Fine-tuning, RL Reasoning

Abstract: The two-stage fine-tuning paradigm of Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has empirically shown better reasoning performance than one-stage SFT for the post-training of Large Language Models (LLMs). However, the evolution and mechanism behind the synergy of SFT and RL are still under-explored and inconclusive. To figure out this issue, we dissect the Out-Of-Distribution (OOD) and In-Distribution (ID) reasoning performance of LLaMA-3.2-11B and Qwen-2.5-7B at different checkpoints of the fine-tuning (full-parameter, rather than LoRA) process, and conduct fine-grained analysis. We find the well-known claim "SFT memorizes, RL generalizes" is over-simplified, and discover that: (1) OOD performance peaks at the early stage of SFT and then declines (OOD forgetting), the best SFT checkpoint cannot be captured by training/test loss; (2) the subsequent RL stage does not generate fundamentally better OOD capability, instead it plays an OOD restoration role, recovering the lost reasoning ability during SFT; (3) The recovery ability has boundaries, i.e., if SFT trains for too short or too long, RL cannot recover the lost OOD ability; (4) To uncover the underlying mechanisms behind the forgetting and restoration process, we employ SVD analysis on parameter matrices, manually edit them, and observe their impacts on model performance. Unlike the common belief that the shift of model capacity mainly results from the changes of singular values, we find that they are actually quite stable throughout fine-tuning. Instead, the OOD behavior strongly correlates with the rotation of singular vectors. In a nutshell, SFT performs hard alignment of the crucial parameter directions to the target tasks, leading to rapid and greedy adjustment, but also quick forgetting; RL then conditionally re-aligns singular vectors softly and slowly towards a more robust configuration, healing the forgetting and learning the downstream tasks simultaneously. Our findings re-identify the roles of SFT and RL in the two-stage fine-tuning and discover the rotation of singular vectors as the key mechanism.