Hack-Verifiable Environments: Towards Evaluating Reward Hacking at Scale
A new paper introduces a method for evaluating reward hacking in autonomous agents. The authors propose embedding detectable reward hacking opportunities into environments for reliable measurement. This approach is instantiated in a testbed called Hack-Verifiable TextArena, which allows for the analysis of reward hacking behavior across various language models.
- ▪The paper addresses the challenge of aligning autonomous agents with human intent, specifically focusing on reward hacking.
- ▪It presents a new evaluation paradigm that allows for the deterministic measurement of reward hacking.
- ▪The authors have released a testbed named Hack-Verifiable TextArena to analyze reward hacking behavior in language models.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20744 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Machine Learning arXiv:2605.20744 (cs) [Submitted on 20 May 2026] Title:Hack-Verifiable Environments: Towards Evaluating Reward Hacking at Scale Authors:Amit Roth, Ankur Samanta, Matan Halevy, Yoav Levine, Yonathan Efroni View a PDF of the paper titled Hack-Verifiable Environments: Towards Evaluating Reward Hacking at Scale, by Amit Roth and 4 other authors View PDF HTML (experimental) Abstract:Aligning autonomous agents with human intent remains a central challenge in modern AI. A key manifestation of this challenge is reward hacking, whereby agents appear successful under the evaluation signal while violating the intended objective. Reward hacking has been observed across a wide range of settings, yet methods for reliably measuring it at scale remain lacking.
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