Harness-agnostic detection and immunization of reward hacking in self-evolving language models
Self-evolving language models improve by proposing candidate updates and keeping whatever raises a visible score. When that score is an imperfect proxy for the capability one actually wants, sustained selection widens the gap between the two. This is reward hacking. We introduce HackProbe, a monitor that attaches to an arbitrary self-evolving loop through two black-box hooks, with no access to weights or activations. It keeps a secret, distribution-fixed comparison core, whose frozen distribution makes its capability proxy comparable across generations, alongside a rotated fresh layer that hardens the bank against co-adaptation. Four tests built on that proxy cover the level gap, a scale-...
| Authors | Rongxin Yang, Yang Liu, Shang Luo et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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