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cs.LG2026
ReLibra: Routing-Replay-Guided Load Balancing for MoE Training in Reinforcement Learning
Chao Jin, Xinming Wei, Yinmin Zhong +6
Load imbalance is a long-standing challenge in Mixture-of-Experts (MoE) training and is exacerbated in reinforcement learning (RL) for LLMs, where hot experts can shift frequently…
cs.LG2026
BigMac: Breaking the Pareto Frontier of Compute and Memory in Multimodal LLM Training
Zili Zhang, Chengxu Yang, Shenglong Zhang +8
Training multimodal large language models (MLLMs) is challenged by both model and data heterogeneity. Existing systems redesign the training pipeline to address these challenges, b…
cs.LG2026
Heddle: A Distributed Orchestration System for Agentic RL Rollout
Zili Zhang, Yinmin Zhong, Chengxu Yang +5
Agentic Reinforcement Learning (RL) enables LLMs to solve complex tasks by alternating between a data-collection rollout phase and a policy training phase. During rollout, the agen…