Escaping the Context Bottleneck: Active Context Curation for LLM Agents via Reinforcement Learning
Large Language Models (LLMs) struggle with long-horizon tasks due to the "context bottleneck" and the "lost-in-the-middle" phenomenon, where accumulated noise from verbose environments degrades reasoning over multi-turn interactions. To address this issue, we introduce a symbiotic framework that decouples context management from task execution. Our architecture pairs a lightweight, specialized policy model, ContextCurator, with a powerful frozen foundation model, TaskExecutor. Trained via reinforcement learning, ContextCurator actively reduces information entropy in the working memory. It aggressively prunes environmental noise while preserving reasoning anchors, that is, sparse data poin...
| Authors | Xiaozhe Li, Tianyi Lyu, Yizhao Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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