3 papers
cs.AI2025
Enhancing Reasoning through Process Supervision with Monte Carlo Tree Search
Shuangtao Li, Shuaihao Dong, Kexin Luan +2
Large language models (LLMs) have demonstrated their remarkable capacity across a variety of tasks. However, reasoning remains a challenge for LLMs. To improve LLMs' reasoning abil…
cs.LG2024
Towards Intrinsic Self-Correction Enhancement in Monte Carlo Tree Search Boosted Reasoning via Iterative Preference Learning
Huchen Jiang, Yangyang Ma, Chaofan Ding +2
With current state-of-the-art approaches aimed at enhancing the reasoning capabilities of Large Language Models(LLMs) through iterative preference learning inspired by AlphaZero, w…
cs.CL2024
Low-Rank Adaptation with Task-Relevant Feature Enhancement for Fine-tuning Language Models
Changqun Li, Chaofan Ding, Kexin Luan +1
Fine-tuning pre-trained large language models in a parameter-efficient manner is widely studied for its effectiveness and efficiency. LoRA is one of the most widely used methods, w…