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

---
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
