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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 c
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