agent/papers/items/2026-2606-09961-3spo-state-score-supervised-policy-optimization-for-llm-agents.md

Paper: 3SPO: State-Score-Supervised Policy Optimization for LLM Agents

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type: paper title: "3SPO: State-Score-Supervised Policy Optimization for LLM Agents" authors: Yu Han, Kailing Li, Yang Jiao, Yulin Dai, Yuqian Fu, Linhai Zhuo, Tianwen Qian year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.09961 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-08 updated_at: 2026-06-08 status: queued relevance: high topics:

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

benchmarks:

models:

datasets:

• cs.LG
• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 13 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: computer-use, planning, tool-use
• arXiv categories: cs.LG, cs.AI
• collection score: 13

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/2606.09961