5 papers
How Much, Then Where: Credit-Conserving Action-to-Token Allocation for Multi-Turn Agent Reinforcement Learning
Lichao Ma, Yang Sun, Shuaitao Zhao +9
Credit assignment in multi-turn agent reinforcement learning operates at two levels: assigning trajectory-level credit to actions and distributing each action's credit across its t…
Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation
Yi Yang, Cong Qin, Xiaodan Liu +8
Large language model agents are commonly trained through reinforcement learning with sparse trajectory-level rewards, which offer limited guidance on how strongly individual tokens…
Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning
Chishui Chen, Jiaye Lin, Te Sun +6
Agent skills are callable procedural modules that provide reusable knowledge and execution policies for complex agentic tasks. However, existing methods mainly focus on selecting r…
Placing Puzzle Pieces Where They Matter: A Question Augmentation Framework for Reinforcement Learning
Yangyi Fang, Jiaye Lin, Xiaoliang Fu +2
Reinforcement learning has become a powerful approach for enhancing large language model reasoning, but faces a fundamental dilemma: training on easy problems can cause overfitting…
From to : Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight
Xiaoliang Fu, Jiaye Lin, Yangyi Fang +6
Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed a leap in Large Language Model (LLM) reasoning, yet its optimization dynamics remain fragile. Standard algorithm…