3 papers
cs.CL2026
Co-Evolving Skill Generation and Policy Optimization
Zhiwei Zhang, Yudi Lin, Nikki Lijing Kuang +4
Skill-augmented reinforcement learning improves language agents by storing reusable procedural knowledge acquired from past experience. Existing methods typically use strong langua…
cs.LG2026
LARK: Learnability-Grounded Trajectory Selection for Efficient Reasoning Distillation
Tianrun Yu, Kaixiang Zhao, Chih-Chun Chen +5
We study trajectory selection for reasoning distillation, where teacher-generated reasoning trajectories are selectively used as supervision for a student model. Existing methods r…
cs.LG2025
Stragglers Can Contribute More: Uncertainty-Aware Distillation for Asynchronous Federated Learning
Yujia Wang, Fenglong Ma, Jinghui Chen
Asynchronous federated learning (FL) has recently gained attention for its enhanced efficiency and scalability, enabling local clients to send model updates to the server at their…