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Multiscale Policy Optimization for Long-Horizon LLM Agents

379 upvotes · 26 OCT 2026 · Tao Ren, Hui Yang, Jia Deng et al.

This paper proposes a new optimization technique for long-horizon language model agents, allowing them to make more informed decisions by allocating credit more finely across individual tokens. Practitioners might care because it could lead to better performance in tasks like search, tool-use, and automation.

reinforcement learninglong-horizon planningimportance samplingpolicy optimizationlanguage models