3 papers
cs.CL2026
Distilling Token-Trained Models into Byte-Level Models
Zishuo Bao, Jiaqi Leng, Junxiong Wang +2
Byte Language Models (BLMs) have emerged as a promising direction for scaling language models beyond tokenization. However, existing BLMs typically require training from scratch on…
cs.CL2025
Understanding and Improving Length Generalization in Hierarchical Sparse Attention Models
Jiaqi Leng, Xiang Hu, Junxiong Wang +3
Effectively processing long contexts is a critical challenge for language models. While standard Transformers are limited by quadratic complexity and poor length extrapolation, alt…
cs.CL2025
Hardware-aligned Hierarchical Sparse Attention for Efficient Long-term Memory Access
Xiang Hu, Jiaqi Leng, Jun Zhao +2
A key advantage of Recurrent Neural Networks (RNNs) over Transformers is their linear computational and space complexity enables faster training and inference for long sequences. H…