EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Zhilin Wang, Han Song, Runzhe Zhan, Jusen Du, Jiacheng Chen, Tianle Li, Qingyu Yin, Yulun Wu, Zhennan Shen, Tong Zhu, Yanshu Li, Guanjie Chen, Derek F. Wong, Yafu Li, Yu Cheng, Yang Yang
• Venue: arXiv: cs.AI
• Published: 2026-07-02
• Collected: 2026-07-03
• URL: http://arxiv.org/abs/2607.02440v1
• PDF: https://arxiv.org/pdf/2607.02440v1
• DOI: UNKNOWN

Plain-Language Brief

In the research paper titled 'EvoPolicyGym', the authors introduce a controlled evaluation setting called Autonomous Policy Evolution (APE) for assessing how autonomous agents iteratively improve their executable policies in interactive environments. The study uses EvoPolicyGym, a benchmark created from compact Reinforcement Learning (RL) environments, to evaluate agent performance. The paper states that GPT-5.5 performs best on the suite, achieving top ranks in all 16 environments. It also provides diagnostic analyses showing how agents allocate their budget and convert feedback into parametric tuning.

What the Document Says

• The study introduces a controlled evaluation setting called Autonomous Policy Evolution (APE).
• EvoPolicyGym is a benchmark created from compact interactive RL environments for evaluating autonomous agents.
• GPT-5.5 achieves the strongest aggregate rank score and top-two performance on all 16 environments in the EvoPolicyGym suite.
• The study provides trajectory-level diagnostics that show how agents allocate their budget and convert feedback into parametric tuning.

Key Concepts Mentioned

• Autonomous Policy Evolution (APE)
• EvoPolicyGym
• Reinforcement Learning (RL) environments
• GPT-5.5

Claims Made by the Document

• Strong autonomous policy evolution depends not only on isolated task wins, but on discovering task-appropriate mechanisms and refining policies under bounded feedback.

Evidence Used

• The study presents results from the EvoPolicyGym suite where GPT-5.5 achieves top ranks in all 16 environments.
• The paper provides trajectory-level diagnostics showing how agents allocate their budget and convert feedback into parametric tuning.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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 environments. Beyond leaderboard results, EvoPolicyGym also provides trajectory-level diagnostics that distinguish how agents allocate budget, convert feedback into parametric tuning. These analyses show that strong autonomous policy evolution depends not only on isolated task wins, but on discovering task-appropriate mechanisms and refining policies under bounded feedback.