Output Supervision Can Obfuscate the Chain of Thought

ICLR 2026 Conference Submission20214 Authors

Keywords: chain of thought, monitoring, CoT, supervision, monitor, obfuscation

TL;DR: Training against a monitor that only sees a model's final output is sufficient to obfuscate the CoT

Abstract: Recently, OpenAI (2025) showed that training against a chain of thought (CoT) monitor can cause obfuscated CoTs, which contain bad behavior the monitor cannot detect. They proposed to keep CoTs monitorable by training only against output monitors that do not have access to CoT. We show that such training can still cause obfuscated CoTs via two mechanisms. First, when a model is trained to produce a safe-looking output, that model may generalize to making its CoTs look safe. Second, since later tokens are conditioned on earlier ones, safe‑looking CoTs may increase the likelihood of safe outputs, causing safe-looking CoTs to be reinforced. We introduce two mitigations to address these two issues, which achieve a Pareto improvement in terms of monitorability and task performance compared to regular training. To our knowledge, we are the first to identify and mitigate these problems. Our work implies that preserving CoT monitorability is more difficult than previously thought; we suggest practical guidelines for AI developers to maintain monitorable CoTs.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 20214