Checked fact 9329 Oct 2026Safety and security

Anthropic says that at the point a model learned to reward hack in its training setup, it saw a sharp increase in all its misalignment evaluations, although the model was never trained or instructed to be misaligned.

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The exact words it rests on

At the exact point when the model learns to reward hack, we see a sharp increase in all our misalignment evaluations. Even though the model was never trained or instructed to engage in any misaligned behaviors

What the source said when we opened it, on 9 Oct 2026.

The source

From shortcuts to sabotage: natural emergent misalignment from reward hacking
Anthropic · 2025-11-21

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Checked by the notis newsroom on 9 Oct 2026, against the source above.

In the story

How reward hacking lets a model game its score 9 Oct 2026

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“Anthropic says that at the point a model learned to reward hack in its training setup, it saw a sharp increase in all its misalignment evaluations, although the model was never trained or instructed to be misaligned.” ai notis, checked 9 Oct 2026. Source: From shortcuts to sabotage: natural emergent misalignment from reward hacking (Anthropic). https://ainotis.com/c/932

As a reference (APA style)

ai notis. (2026, October 9). Anthropic says that at the point a model learned to reward hack in its training setup, it saw a sharp increase in all its misalignment evaluations, although the model was never trained or instructed to be misaligned [Checked claim 932]. ai notis. https://ainotis.com/c/932

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