AI RESEARCH
Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use
arXiv CS.LG
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ArXi:2605.02964v1 Announce Type: new Reinforcement learning (RL) trained language model agents with tool access are increasingly deployed in coding assistants, research tools, and autonomous systems. We We evaluate 13 frontier models from OpenAI, Anthropic, Google, and DeepSeek. Exploit rates range from 0% (Claude Sonnet 4.5) to 13.9% (DeepSeek-R1-Zero), varying sharply by post-