11 citations · 20 across the 11 of their papers we have counts for
3 papers · 1 filter
MobileLLM-R1: Exploring the Limits of Sub-Billion Language Model Reasoners with Open Training Recipes
Changsheng Zhao, Ernie Chang, Zechun Liu +8
The paradigm shift in large language models (LLMs) from instinctive responses to chain-of-thought (CoT) reasoning has fueled two prevailing assumptions: (1) reasoning capabilities…
Param for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost
Sheng Cao, Mingrui Wu, Karthik Prasad +2
The post-training phase of large language models is essential for enhancing capabilities such as instruction-following, reasoning, and alignment with human preferences. However, it…
You Only Use Reactive Attention Slice For Long Context Retrieval
Yun Joon Soh, Hanxian Huang, Yuandong Tian +1
Supporting longer context for Large Language Models (LLM) is a promising direction to advance LLMs. As training a model for a longer context window is computationally expensive, ma…