agent/papers/items/2026-2606-17680-envrl-learn-from-environment-dynamics-in-agentic-reinforcement-learning.md

Paper: EnvRL: Learn from Environment Dynamics in Agentic Reinforcement Learning

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type: paper title: "EnvRL: Learn from Environment Dynamics in Agentic Reinforcement Learning" authors: Zhitong Wang, Songze Li, Hao Peng, Shuzheng Si, Yi Wang, Maosong Sun, Juanzi Li year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.17680 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-16 updated_at: 2026-06-16 status: queued relevance: high topics:

• agent-evaluation
• planning
• rag
• tool-use methods:

benchmarks:

models:

datasets:

• cs.LG
• cs.CL related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 15 collection_queries: agent-evaluation

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: agent-evaluation
• inferred topics: agent-evaluation, planning, rag, tool-use
• arXiv categories: cs.LG, cs.CL
• collection score: 15

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.17680