3 papers
cs.LG2025
Hierarchical Balance Packing: Towards Efficient Supervised Fine-tuning for Long-Context LLM
Yongqiang Yao, Jingru Tan, Kaihuan Liang +7
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 ma…
cs.AI2025
OmniBal: Towards Fast Instruction-Tuning for Vision-Language Models via Omniverse Computation Balance
Yongqiang Yao, Jingru Tan, Feizhao Zhang +8
Vision-language instruction-tuning models have recently achieved significant performance improvements. In this work, we discover that large-scale 3D parallel training on those mode…
cs.CV2025
Visual Programmability: A Guide for Code-as-Thought in Chart Understanding
Bohao Tang, Yan Ma, Fei Zhang +6
Chart understanding presents a critical test to the reasoning capabilities of Vision-Language Models (VLMs). Prior approaches face critical limitations: some rely on external tools…