8 papers
HSAP: A Hierarchical Sequence-aware Parallelism for Hybrid-Context Generative Models
Songxin Zhang, Zejian Xie, Zhuoyang Song +4
In this paper, we aim to combine the advantages of existing sequence parallelism paradigms and overcomes their drawbacks, the most serious of which is the incapability to correctly…
BatchWeave: A Consistent Object-Store-Native Data Plane for Large Foundation Model Training
Ting Sun, Junjie Zhang, Xiao Yan +7
Modern Large Foundation Model (LFM) training has transformed the data pipeline from a static ingestion layer into a dynamic component that must co-evolve with the training process.…
Natural Language-Driven Global Mapping of Martian Landforms
Yiran Wang, Shuoyuan Wang, Zhaoran Wei +7
Planetary surfaces are typically analyzed using high-level semantic concepts in natural language, yet vast orbital image archives remain organized at the pixel level. This mismatch…
Orcust: Stepwise-Feedback Reinforcement Learning for GUI Agent
Junyu Lu, Songxin Zhang, Zejian Xie +2
Recent advances in GUI agents have achieved remarkable grounding and action-prediction performance, yet existing models struggle with unreliable reward signals and limited online t…
L0: Reinforcement Learning to Become General Agents
Junjie Zhang, Jingyi Xi, Zhuoyang Song +7
Training large language models (LLMs) to act as autonomous agents for multi-turn, long-horizon tasks remains significant challenges in scalability and training efficiency. To addre…
Exploring Learning Complexity for Efficient Downstream Dataset Pruning
Wenyu Jiang, Zhenlong Liu, Zejian Xie +3
The ever-increasing fine-tuning cost of large-scale pre-trained models gives rise to the importance of dataset pruning, which aims to reduce dataset size while maintaining task per…