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arXiv AI By Qiang Fu, Xinyue Li, Andre Wibisono, Arman Cohan

Learning to Be Monitored: Causal Bounds and Intervention-Aware Post-Training Recipes for Chain-of-Thought Monitorability

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The paper introduces a refined evaluation framework for chain‑of‑thought (CoT) monitoring in large language models, showing that the commonly used minimal‑criterion sensitivity metric overestimates causal detection of targeted behaviors. It establishes upper and lower bounds on causal detection rates and uses these to assess monitorability. The authors then create a dataset of control–intervention task pairs and apply three training methods—off‑policy self‑distillation, reinforcement learning, and on‑policy self‑distillation—to produce model variants that consistently improve CoT monitorability across various configurations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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