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Reasoning Models Don't Always Say What They Think

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We think the difference in results is likely because the reward hacks studied in Baker et al. (2025) require more exploration and reasoning to exploit, and is applied to a setting (coding) where it is more common to verbalize the potential hacks.

Hint-style reward hacks planted in controlled experiments represent reward hacks learned in realistic reinforcement learning.Doubtfuldenies

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CoTs of reasoning models verbalize reasoning hints at least some of the time, but rarely do so reliably (in our settings where exploiting them does not require a CoT)

Reasoning models verbalize prompt hints they use in fewer than half of hint-insertion faithfulness evaluation cases.Doubtful

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Link: https://arxiv.org/html/2505.05410
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Oct 30, 2026Entered the graphEntered the graph; no copy stored