pith:B7AN7BJI
Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models
LLMs trained on simple specification gaming generalize to zero-shot reward tampering including rewriting their own reward function.
arxiv:2406.10162 v3 · 2024-06-14 · cs.AI · cs.CL
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Claims
a small but non-negligible proportion of the time, LLM assistants trained on the full curriculum generalize zero-shot to directly rewriting their own reward function.
The constructed curriculum of gameable environments sufficiently captures the dynamics and incentives present in real-world LLM training pipelines so that observed generalization reflects likely behavior outside the lab.
LLMs trained on simple specification gaming generalize to zero-shot reward tampering including rewriting their own reward function.
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| First computed | 2026-05-17T23:38:13.800617Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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