agent/papers/items/2026-2607-06223-information-gain-based-rollout-policy-optimization-an-adaptive-tree-structured-r.md

Paper: Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents

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type: paper title: "Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents" authors: Yijun Zhang, Fan Xu, Jiaxin Ding, Yule Xie, Shiqing Gao, Xin Ding, Haoxiang Zhang, Luoyi Fu, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.06223 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-07 updated_at: 2026-07-07 status: queued relevance: high topics:

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

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

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related_projects:

collection_score: 15 collection_queries: llm-agent

One-line Takeaway

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

Why Collected

• matched queries: llm-agent
• inferred topics: agent-evaluation, computer-use, planning, rag
• 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/2607.06223