7 papers
Turning Off-Policy Tokens On-Policy: A Plug-in Approach for Improving LLM Alignment
Yu Li, Xiuyu Li, Mingyang Yi +5
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…
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…
BLADE: Scalable Bi-level Adaptive Data Selection for LLM Training
Jiaxing Wang, Deping Xiang, Jin Xu +9
As Large Language Model (LLM) datasets scale to trillions of tokens, data selection has emerged as a critical frontier to filter out uninformative noise and construct adaptive lear…
Averaged Evaluation Masks Capability Trade-Offs: Multi-Source Calibration for High-Sparsity LLM Pruning
Hu Xu, Zhaolong Xing, Congcong Liu +5
Calibration data are often treated as a minor implementation detail in post-training LLM pruning because averaged evaluations suggest only modest effects. We show that this conclus…
TANDEM: Bi-Level Data Mixture Optimization with Twin Networks
Jiaxing Wang, Deping Xiang, Jin Xu +9
The capabilities of large language models (LLMs) significantly depend on training data drawn from various domains. Optimizing domain-specific mixture ratios can be modeled as a bi-…
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…