Cheap Reward Hacking Detection

Iván Belenky; Joaquín Itria; Steven Johns

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arXiv · 2606.08893

Cheap Reward Hacking Detection

Iván Belenky·Joaquín Itria·Steven Johns

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Abstract

A small transformer encoder is trained to map Terminal-Wrench trajectories onto a unit sphere where embedding distance approximates the $L_1$ distance between reward and metadata signals. A linear probe on top of that embedding detects reward hacking on the cleaned test split with AUC $0.9467$ and TPR@5%FPR $0.8296$, matching the TW sanitized LLM-as-judge AUC ($0.9510$ on the cleaned split) and exceeding its TPR@5%FPR ($0.7130$ vs $0.8296$) on the same information condition, at roughly four orders of magnitude lower per-trajectory cost. The encoder is not a pure behavior reader: stripping natural-language reasoning from its input at probe time drops AUC to $0.6213$.

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BibTeX RIS

Iván Belenky, Joaquín Itria, Steven Johns. 2026-06-08. Cheap Reward Hacking Detection. https://arxiv.org/abs/2606.08893

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