11 papers
Retrieval Grounding Latent Reasoning for Dense Retrieval
Gang Zhou, Xiongxi Yu, Hu Tian +5
Reasoning-intensive retrieval requires text representations to capture not only semantic similarity, but also the reasoning needed to determine relevance under a given retrieval in…
Long SKILL Compliance as Logical Reasoning: Closure-Grounded Detection with Scaling-Guided On-Policy Distillation
Shuaitao Zhao, Feng Ni, Lichao Ma +4
The increasing complexity of enterprise business scenarios has promoted the widespread adoption of long SKILL documents in agent systems, posing new challenges for compliance detec…
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…
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…
Rethinking Continual Experience Internalization for Self-Evolving LLM Agents
Jingwen Chen, Wenkai Yang, Shengda Fan +7
Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward continual learning in large l…