agent/papers/items/2026-2606-03108-evotrainer-co-evolving-llm-policies-and-training-harnesses-for-autonomous-agenti.md

Paper: EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning

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type: paper title: "EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning" authors: Guhong Chen, Yingcheng Shi, Yongbin Li, Binhua Li, Xander Xu, Hu Wei, Shiwen Ni, Min Yang, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.03108 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-02 updated_at: 2026-06-12 status: queued relevance: high topics:

• agent-evaluation
• coding-agent
• planning
• reasoning methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 15 collection_queries: autonomous-agent-llm

One-line Takeaway

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

Why Collected

• matched queries: autonomous-agent-llm
• inferred topics: agent-evaluation, coding-agent, planning, reasoning
• arXiv categories: cs.AI
• 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/2606.03108