Large Reasoning Models Learn Better Alignment from Flawed Thinking
Authors: name: ShengYun Peng, name: Eric Smith, name: Ivan Evtimov, name: Song Jiang, name: Pin-Yu Chen, name: Hongyuan Zhan, name: Haozhu Wang, name: Duen Horng Chau, name: Mahesh Pasupuleti, name: Jianfeng Chi
Organization: MetaSuperintelligenceLab

Large reasoning models (LRMs) "think" by generating structured
chain-of-thought (CoT) before producing a final answer, yet they still lack the
ability to reason critically about safety alignment and are easily biased when
a flawed premise is injected into their thought process. We propose RECAP
(Robust Safety Alignment via Counter-Aligned Prefilling), a principled
reinforcement learning (RL) method for post-training that explicitly teaches
models to override flawed reasoning trajectories and reroute to safe and
helpful responses. RECAP trains on a mixture of synthetically generated
counter-aligned CoT prefills and standard prompts, requires no additional
training cost or modifications beyond vanilla reinforcement learning from human
feedback (RLHF), and substantially improves safety and jailbreak robustness,
reduces overrefusal, and preserves core reasoning capability -- all while
maintaining inference token budget. Extensive analysis shows that RECAP-trained
models engage in self-reflection more frequently and remain robust under
adaptive attacks, preserving safety even after repeated attempts to override
their reasoning.