The Ends Justify the Thoughts: RL-Induced Motivated Reasoning Fools CoT Monitors
Chain-of-thought (CoT) monitoring has emerged as a compelling method for detecting harmful behaviors such as reward hacking in reasoning models, under the assumption that models' reasoning processes are informative of such behaviors. However, when a model's trained behaviors conflict with instructions provided at inference time, it faces two competing objectives it cannot simultaneously satisfy. We find that models resolve this tension through motivated reasoning---generating plausible-sounding justifications for violating their instructions while downplaying potential harms or contradictions---rather than genuine engagement with the spirit of the provided instructions. We investigate this phenomenon by training reasoning models via RL on objectives that conflict with their later constitutional constraints, and find that motivated reasoning emerges systematically across all settings studied, including beyond safety-related tasks. As motivated reasoning becomes more prevalent over the course of training, CoT monitors are increasingly fooled. Concerningly, the same monitor that correctly flags a violation when shown only the final answer is persuaded to approve it when shown the reasoning trace. While large reasoning models closely track human ability in detecting motivated reasoning, this is not a full solution, since frontier model providers commonly rely on smaller models for monitoring due to latency and cost constraints. Our results underscore the necessity for further research into the emergence and detection of motivated reasoning in model evaluation and oversight. Code for this paper is available at https://github.com/nikihowe/motivated-reasoning.
Chain-of-thought (CoT) monitoring has emerged as a compelling method for detecting harmful behaviors such as reward hacking in reasoning models, under the assumption that models' reasoning processes are informative of such behaviors. However, when a model's trained behaviors conflict with instructions provided at inference time, it faces two competing objectives it cannot simultaneously satisfy. We find that models resolve this tension through motivated reasoning---generating plausible-sounding justifications for violating their instructions while downplaying potential harms or contradictions---rather than genuine engagement with the spirit of the provided instructions. We investigate this phenomenon by training reasoning models via RL on objectives that conflict with their later constitutional constraints, and find that motivated reasoning emerges systematically across all settings studied, including beyond safety-related tasks. As motivated reasoning becomes more prevalent over the course of training, CoT monitors are increasingly fooled. Concerningly, the same monitor that correctly flags a violation when shown only the final answer is persuaded to approve it when shown the reasoning trace. While large reasoning models closely track human ability in detecting motivated reasoning, this is not a full solution, since frontier model providers commonly rely on smaller models for monitoring due to latency and cost constraints. Our results underscore the necessity for further research into the emergence and detection of motivated reasoning in model evaluation and oversight. Code for this paper is available at https://github.com/nikihowe/motivated-reasoning.