EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments
Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a final score or confound it with open-ended software-engineering progress. We introduce Autonomous Policy Evolution, a controlled evaluation setting in which a harness-model agent repeatedly edits an executable policy system under a fixed interaction budget. We instantiate this setting in EvoPolicyGym, a benchmark built from compact interactive RL environments that evaluates how agents iteratively improve explored policies. On the EvoPolicyGym suite, GPT-5.5 achieves the strongest aggregate rank score and top-two performance on all 16 envi...
| Authors | Zhilin Wang, Han Song, Runzhe Zhan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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