Explore/benchmark/BAITBENCH: Measuring Agent Reward Hacking with Optional Shortcuts Planted in ML Tasks
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Pradyumna Shyama Prasad, Meiri Anto, Leon Eshuijs, Julian Moncarz, Kaustubh Kislay, Juan J. Vazquez/BAITBENCH: Measuring Agent Reward Hacking with Optional Shortcuts Planted in ML TasksUnknown

BAITBENCH is a benchmark suite of synthetic tabular ML tasks that contain optional shortcuts enabling agents to inflate test scores, designed to measure reward hacking in autonomous agents.

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Production readinessResearch / EarlyBest for exploration and prototyping.
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AgentHub Score
55 / 100
Composite score from 6 signals. How we score →
Active project
55Score
Growth
40C
Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
Production
58C
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Practical assessment
Should you use it?

✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

⚠ Watch-outs

  • Review changelog before updating
  • Verify license for commercial use
Technical details
What's inside
Language
License
Sourcearxiv
Open source✗ No
Commercial use
Docs
Demo

AgentHub Score

55
Score 55/100
Below average

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