Reasoning-Preserving Fine-Tuning of Post-RL LLMs with Null-Basis LoRA
Reinforcement learning (RL)-based post-training has become an effective approach for eliciting reasoning capabilities in large language models (LLMs). However, adapting post-RL models to new knowledge domains or behaviors through subsequent supervised fine-tuning (SFT) can severely overwrite these capabilities. Existing approaches mitigate such forgetting through experience replay, specialized initialization, or constrained optimization using gradient projection, but either provide limited preservation or incur substantial training overhead. Our analysis shows that reasoning activations concentrate in low-dimensional subspaces, leaving substantial null-space capacity for adaptation, and t...
| Authors | Wenzhi Fang, Nicholas Tzou, Lazar Valkov et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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