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Feizhao Zhang

2 accepted papers

2025

Hierachical Balance Packing: Towards Efficient Supervised Fine-tuning for Long-Context LLM

NeurIPS 2025poster

Training Long-Context Large Language Models (LLMs) is challenging, as hybrid training with long-context and short-context data often leads to workload imbalances. Existing works mainly use data packing to alleviate this issue, but fail to consider imbalanced attention computation and wasted communic…

Cited by 0SourcecodeScholar
2025

OmniBal: Towards Fast Instruction-Tuning for Vision-Language Models via Omniverse Computation Balance

ICML 2025poster

Vision-language instruction-tuning models have recently achieved significant performance improvements. In this work, we discover that large-scale 3D parallel training on those models leads to an imbalanced computation load across different devices. The vision and language parts are inherently hetero…