agent/papers/items/2026-2607-05378-compactionrl-reinforcement-learning-with-context-compaction-for-long-horizon-age.md

Paper: CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents

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type: paper title: "CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents" authors: Yujiang Li, Zhenyu Hou, Yi Jing, Jie Tang, Yuxiao Dong year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.05378 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-06 updated_at: 2026-07-06 status: queued relevance: high topics:

• coding-agent
• planning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.LG related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 14 collection_queries: coding-agent, llm-agent

One-line Takeaway

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

Why Collected

• matched queries: coding-agent, llm-agent
• inferred topics: coding-agent, planning, tool-use
• arXiv categories: cs.LG
• collection score: 14

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/2607.05378