3 papers
cs.LG2026
Training-Free Halving of Activated Experts in Fine-Grained Mixture-of-Experts Models
Xing Chen, Hengshuai Yao
Modern fine-grained Mixture-of-Experts (MoE) models route each token to a small number of experts and renormalize their router probabilities. We show that this renormalization impl…
cs.LG2023
Careful at Estimation and Bold at Exploration
Xing Chen, Yijun Liu, Zhaogeng Liu +3
Exploration strategies in continuous action space are often heuristic due to the infinite actions, and these kinds of methods cannot derive a general conclusion. In prior work, it…
cs.LG2022
The Sufficiency of Off-Policyness and Soft Clipping: PPO is still Insufficient according to an Off-Policy Measure
Xing Chen, Dongcui Diao, Hechang Chen +7
The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space. Does there exist better policies outside of this space? By using a nov…