Author

Gen Li

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Preprint Aug 2026

RoutePack: Expert Placement and Attention-Aware Data Packing for MoE Reinforcement Learning

Training Mixture-of-Experts (MoE) models for reinforcement learning (RL) couples two load-balancing problems: sequence composition determines dense attention work in each data-parallel microbatch, while token routing determines sparse expert work on expert-parallel ranks. Optimizing either alone can shift the bottlenec...

Yibo Shen, Xudong Han, Xiao-Wei Zhu et al. · 0 citations

#machine learning Preprint Aug 2026

HARTS: Efficient Agentic Reinforcement Learning for Hybrid-Attention Models over Arbitrary Rollout Trees

HARTS is the first system to demonstrate arbitrary-rollout-tree prefix-sharing speedups on a real hybrid-attention model, and its numerical differences are comparable to baseline self-rerun variation, and its reward trend is similar to the baseline over the first 120 steps of SWE-bench training.

Bo-Yuan Meng, Pei-Hua Bao, Hong Liu et al. · 0 citations