Large Language Models

A Unifying Lens on Supervised Fine-Tuning Through Target Distribution Design
Supervised fine-tuning (SFT) typically maximizes the likelihood of every token in a demonstrated trajectory. However, an observed token …
Tong Xie, Yuanhao Ban, Yunqi Hong, Sohyun An, Yihang Chen, Cho-Jui Hsieh
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Don't Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models
While recent success of large reasoning models (LRMs) significantly advanced LLMs’ reasoning capability by optimizing the final …
Sohyun An, Ruochen Wang, Tianyi Zhou, Cho-Jui Hsieh
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One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts
Large Language Models (LLMs) exhibit strong generalization capabilities to novel tasks when prompted with language instructions and …
Ruochen Wang*, Sohyun An*, Minhao Cheng, Tianyi Zhou, Sung Ju Hwang, Cho-Jui Hsieh
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