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“Learned Chain-of-Thought Obfuscation Generalises to Unseen Tasks” by Nathaniel Mitrani, sassanb, Cam Tice, Puria

Published 2 weeks, 1 day ago
Description

TL;DR

Training against a CoT or summary-only monitor can lead to obfuscation of dangerous reasoning in unseen tasks. This strengthens the “don’t train against a monitor” claims.

Figure 1. A Two prior results: penalising the CoT or final response produces obfuscation within the training distribution (Baker et al. 2025; Skaf et al. 2025), and learned reward hacking (RH) generalises across tasks (Nishimura-Gasparian et al. 2024). We combine them and find that obfuscation itself generalises: pressure on the CoT or summary, combined with RH reinforcement, produces obfuscated reasoning on held-out tasks. B The penalty is applied either to the CoT or to the post-CoT summary; everything else is held fixed. C Leave-one-out round robin over four RH datasets, regularised with MMLU, and evaluated on the held-out dataset plus a novel high-stakes medical sycophancy eval. The effect replicates across Qwen3-4B and Qwen3-8B and survives removing the training-time XML formatting at evaluation.

Setup

Environments. We adapt four reward-hacking datasets from Nishimura-Gasparian et al. (2024). All four are multiple-choice questions with leaked information wrapped in XML tags; the model is rewarded for using the leaked information even though the system prompt instructs it to ignore it. The four core tasks are:

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    Outline:

    (00:14) TL;DR

    (00:28) en-US-AvaMultilingualNeural__ Diagram showing AI model training with penalization leading to obfuscated reasoning and behavior generalization across settings.

    (01:43) Setup

    (04:51) CoT obfuscation generalises across tasks under CoT pressure

    (06:01) Output supervision alone induces CoT obfuscation that generalises out-of-distribution

    (07:30) Obfuscation is not an artifact of training-time formatting

    (08:39) Obfuscation strategies

    (09:41) Limitations

    (10:40) Implications

    (12:03) Appendix

    (12:06) Example of obfuscated reasoning under summary penalisation

    (12:12) en-US-AvaMultilingualNeural__ Educational prompt showing correct burn first aid versus dangerous essential oil misinformation.

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    First published:
    May 21st, 2026

    Source:
    https://www.lesswrong.com/posts/HPqRsgSzgQd5HQsrB/learned-chain-of-thought-obfuscation-generalises-to-unseen

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    Narrated by TYPE III AUDIO.

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    Images from the article:

    Diagram showing AI model training with penalization leading to obfuscated reasoning and behavior generalization across settings.
    Multiple-choice question prompt interface with two example scenarios displayed.
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