3 papers
cs.LG2026
Principled Fast and Meta Knowledge Learners for Continual Reinforcement Learning
Ke Sun, Hongming Zhang, Jun Jin +4
Inspired by the human learning and memory system, particularly the interplay between the hippocampus and cerebral cortex, this study proposes a dual-learner framework comprising a…
cs.DC2025
HAP: Hybrid Adaptive Parallelism for Efficient Mixture-of-Experts Inference
Haoran Lin, Xianzhi Yu, Kang Zhao +7
Current inference systems for Mixture-of-Experts (MoE) models primarily employ static parallelization strategies. However, these static approaches cannot consistently achieve optim…
cs.LG2024
Distributional Reinforcement Learning with Regularized Wasserstein Loss
Ke Sun, Yingnan Zhao, Wulong Liu +2
The empirical success of distributional reinforcement learning (RL) highly relies on the choice of distribution divergence equipped with an appropriate distribution representation.…