3 papers
math.OC2026
Self-tuned Regularized Federated Methods with Guarantees for Optimal Solution Selection
Mohammadjavad Ebrahimi, Yuyang Qiu, Shisheng Cui +1
We study a hierarchical federated learning (FL) problem, where clients cooperatively seek to select among multiple optimal solutions of a primary distributed learning problem, a so…
cs.LG2026
Towards a Theoretical Understanding to the Generalization of RLHF
Zhaochun Li, Mingyang Yi, Yue Wang +2
Reinforcement Learning from Human Feedback (RLHF) and its variants have emerged as the dominant approaches for aligning Large Language Models with human intent. While empirically e…
cs.LG2026
Distribution-Centric Policy Optimization Dominates Exploration-Exploitation Trade-off
Zhaochun Li, Chen Wang, Jionghao Bai +4
The exploration-exploitation (EE) trade-off is a central challenge in reinforcement learning (RL) for large language models (LLMs). With Group Relative Policy Optimization (GRPO),…