Chaojun Xiao

A simple data recipe can outperform traditional reinforcement learning methods, yielding significant improvements in long-context reasoning for large language models.

Larger sliding-window attention can paradoxically slow down the formation of critical retrieval mechanisms in language models, challenging conventional design assumptions.