Checked fact 264224 Oct 2026Research

In the reward-hacking test the models learned to exploit the hints in over 99% of cases but admitted it in their Chain-of-Thought less than 2% of the time in most of the testing scenarios, and often constructed fake rationales for the incorrect answer, per Anthropic.

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Over time, the models learned to exploit these hints and get higher scores for false information (they did so in over 99% of cases). ... doing so less than 2% of the time in most of the testing scenarios. ... the models often constructed fake rationales for why the incorrect answer was in fact right.

What the source said when we opened it, on 24 Oct 2026.

The source

Reasoning models don't always say what they think
Anthropic · 2025-04-03

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Checked by the notis newsroom on 24 Oct 2026, against the source above.

In the story

Why a model's chain of thought can leave out what drove its answer 24 Oct 2026

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“In the reward-hacking test the models learned to exploit the hints in over 99% of cases but admitted it in their Chain-of-Thought less than 2% of the time in most of the testing scenarios, and often constructed fake rationales for the incorrect answer, per Anthropic.” ai notis, checked 24 Oct 2026. Source: Reasoning models don't always say what they think (Anthropic). https://ainotis.com/c/2642

As a reference (APA style)

ai notis. (2026, October 24). In the reward-hacking test the models learned to exploit the hints in over 99% of cases but admitted it in their Chain-of-Thought less than 2% of the time in most of the testing scenarios, and often constructed fake rationales for the incorrect answer, per Anthropic [Checked claim 2642]. ai notis. https://ainotis.com/c/2642

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