3 papers
cs.LG2026
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…
cs.CL2026
Look Ahead Before You Distill: Future Trajectory Validation of Teacher Guidance for Agentic On-Policy Distillation
Chishui Chen, Yaoyou Fan, Te Sun +11
On-policy distillation (OPD) provides teacher supervision on states visited by the student, reducing the distribution gap between training and inference. However, in multi-turn age…
cs.CL2026
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…