7 papers
LRMoE: Demand-Side Load Re-Calibration for Efficient MoE Training on Memory-Constrained Nodes
Jiaming Hu, Zhida Jiang, Zhaolong Xing +3
Sparse Mixture-of-Experts (MoE) models offer scalable capacity for Web applications, but uneven routing creates computation and communication bottlenecks. Existing placement and re…
Remember by Asking: Retrieval-Induced Memory Evolution for LLM Agents
Wanqi Zhou, Jiawei Lu, Yang Wang +4
Long-term memory is essential for language agents to maintain coherent and effective behavior over extended, multi-session interactions. Existing memory systems mainly use retrieva…
Turning Off-Policy Tokens On-Policy: A Plug-in Approach for Improving LLM Alignment
Yu Li, Xiuyu Li, Mingyang Yi +4
Reinforcement learning (RL) post-training for large language models (LLMs) follows a efficient paradigm of "rollout then update", which inevitably results in off-policy training da…
Rethinking Calibration for High-Sparsity LLM Pruning: From Single Sources to Capability Coverage
Hu Xu, Zhaolong Xing, Congcong Liu +5
Post-training LLM pruning relies on a small calibration set, usually chosen heuristically and judged by WikiText perplexity or by mean accuracy on general-domain commonsense benchm…
FlowTrain: Flow-Based Decoupled Training for Industrial-Grade Vision-Language Models
Zhida Jiang, Zhaolong Xing, Yang Pei +14
Industrial-grade distributed training of vision-language models (VLMs) remains far less efficient than that of unimodal LLMs. Existing solutions either follow a monolithic design t…
NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining
Zhida Jiang, Zhaolong Xing, Huichao Chai +12
Modern recommendation models have increased to trillions of parameters. As cluster scales expand to O(1k), distributed training bottlenecks shift from computation and memory to dat…
FlexMARL: Graph-Driven Rollout-Training Co-Design for LLM-Based Multi-Agent Reinforcement Learning
Zhida Jiang, Zhaolong Xing, Jiawei Lu +13
Despite algorithm-level innovations for multi-agent reinforcement learning (MARL), the underlying infrastructure for large-scale MARL remains underexplored. Existing training frame…