# EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments

Page: https://stenobird.com/podcast/daily-paper-cast-7079649/evopolicygym-evaluating-autonomous-policy-evolution-in-interactive-environments
Text version: https://stenobird.com/podcast/daily-paper-cast-7079649/evopolicygym-evaluating-autonomous-policy-evolution-in-interactive-environments.md
Podcast: [Daily Paper Cast](https://stenobird.com/podcast/daily-paper-cast-7079649)
Published: 2026-07-04T03:42:54+00:00
Episode link: https://share.transistor.fm/s/f30a665f
Audio file: https://media.transistor.fm/f30a665f/0e4720aa.mp3
Processing state: not_requested
JSON: https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/evopolicygym-evaluating-autonomous-policy-evolution-in-interactive-environments
Duration seconds: 1386

## Resource

🤗 Upvotes: 40 | cs.AI, cs.CL Authors: 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 Title: EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments Arxiv: http://arxiv.org/abs/2607.02440v1 Abstract: 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.

## Actions

- request_transcript: `POST https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/evopolicygym-evaluating-autonomous-policy-evolution-in-interactive-environments/transcription-requests` — Idempotently request low-priority transcript generation for this episode.
- read_markdown: `GET https://stenobird.com/podcast/daily-paper-cast-7079649/evopolicygym-evaluating-autonomous-policy-evolution-in-interactive-environments.md` — Read the agent-friendly Markdown representation of this episode resource.

A page view does not enqueue transcription. Agents should invoke `request_transcript` explicitly when they need this episode processed.

## Transcript

Full transcripts are not published on public pages unless there is a clear rights basis.