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Matching papers

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

Learn What's Left, Not What's Mastered: Saturation Aware Advantage Reweighting for Multi-Reward Policy Optimization

142 upvotes · 17 AUG 2026 · Yixuan Wang, Yifei Chen, Haichao Zhang et al.

This paper improves reinforcement learning for post-training language model reasoners by addressing two problems in existing methods: identical advantages for distinct reward profiles and fixed relative weights for all objectives. A new method, SA-MRPO, dynamically reallocates optimization effort toward under-optimized objectives while maintaining performance on well-satisfied objectives.

reinforcement learningmulti-objective optimizationpolicy optimizationreward engineeringsaturation awareness

DONIC: Generalist Dexterous Bimanual Tracking Control from Human Demonstrations

124 upvotes · 21 OCT 2026 · Mingze Sun, Ziang Cao, Yuan Fei et al.

This paper develops a system that can track both hands of a robot in complex, real-life situations, using demonstrations from humans. Practitioners might care because it could enable robots to perform tasks like assembly, cooking, or surgery with greater precision and flexibility.

dexterous manipulationbimanual trackinghuman-robot interactionreinforcement learningroboticspolicy optimization