Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models 事件

Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models arXiv:2601.18383v2 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) excel at solving complex problems by explicitly generating a reasoning trace before deriving the final answer. However, these extended generations incur substantial memory footprint and computational overhead, bottlenecking LRMs' efficiency. This work uses attention maps to analyze the influence of reasoning traces and uncover