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Tianyu Pang · Vignesh Kothapalli · Shenyang Deng · Haohui Wang · Dawei Zhou · Yaoqing Yang
Vignesh Kothapalli · Rishabh Ranjan · Valter Hudovernik · Vijay Prakash Dwivedi · Johannes Hoffart · Carlos Guestrin · Jure Leskovec
Vignesh Kothapalli · Ata Fatahibaarzi · Hamed Firooz · Maziar Sanjabi
Chain-of-thought (CoT) prompting combined with few-shot in-context learning (ICL) has unlocked significant reasoning capabilities in large language models (LLMs). However, ICL with CoT examples is ineffective on novel tasks when the pre-training knowledge is insufficient. We study this problem in...
Wei-Rui Chen · Vignesh Kothapalli · Ata Fatahibaarzi · Hejian Sang · shao tang · Qingquan Song · Zhipeng Wang · Muhammad Abdul-Mageed
Distilling the capabilities from a large reasoning model (LRM) to a smaller student model often involves training on substantial amounts of reasoning data. However, knowledge distillation (KD) over lengthy sequences with prompt (P), chain-of-thought (CoT), and answer (A) sections makes the proces...