agent/papers/items/2026-2607-14777-seed-self-evolving-on-policy-distillation-for-agentic-reinforcement-learning.md

Paper: SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

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type: paper title: "SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning" authors: Jinyang Wu, Shuo Yang, Zhengxi Lu, Fan Zhang, Yuhao Shen, Lang Feng, Haoran Luo, Zheng Lian, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.14777 code_url: source: arxiv collected_at: 2026-07-27 published_at: 2026-07-16 updated_at: 2026-07-16 status: queued relevance: high topics:

• computer-use
• planning
• tool-use
• workflow-agent methods:

benchmarks:

models:

datasets:

• cs.CL related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 15 collection_queries: tool-use

One-line Takeaway

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

Why Collected

• matched queries: tool-use
• inferred topics: computer-use, planning, tool-use, workflow-agent
• arXiv categories: 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/2607.14777