CheatBench: Measuring Reward Gaming in AI Agents
CheatBench: Measuring Reward Gaming in AI AgentsPaperCodeShow authorsOverviewAI agents increasingly write code, conduct research, and complete professional assignments. They are often trained to earn high rewards for their work. But an agent can also improve its score by cheating: finding hidden answers, copying another agent’s submission, or manipulating how its work is graded.CheatBench measures how often AI agents take these shortcuts when honest work is difficult.
- ▪CheatBench: Measuring Reward Gaming in AI AgentsPaperCodeShow authorsOverviewAI agents increasingly write code, conduct research, and complete professional assignments.
- ▪They are often trained to earn high rewards for their work.
- ▪But an agent can also improve its score by cheating: finding hidden answers, copying another agent’s submission, or manipulating how its work is graded.CheatBench measures how often AI agents take these shortcuts when honest work is difficu
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Record
| Original publisher | Cheatbench |
| Canonical URL | https://www.cheatbench.ai/ |
| Publication time | Thu, 24 Sep 2026 12:01:46 +0000 |
| Retrieval time | 2026-09-24T12:05:26.600Z |
| Last seen | 2026-09-24T12:05:26.600Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | krFWktF8ZZA_ · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
CheatBench: Measuring Reward Gaming in AI AgentsPaperCodeShow authorsOverviewAI agents increasingly write code, conduct research, and complete professional assignments. They are often trained to earn high rewards for their work. But an agent can also improve its score by cheating: finding hidden answers, copying another agent’s submission, or manipulating how its work is graded.CheatBench measures how often AI agents take these shortcuts when honest work is difficult. Its environments pair challenging assignments with opportunities to cheat across ten categories, including mathematics, coding, visual tasks, and knowledge work. We examine the agents’ actions to identify cheating attempts.Cheating varies across models and tasks, and every agent we evaluated cheats in some settings.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Cheatbench.
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