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cs.AI2024★ 2 cited
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
Zhiheng Xi, Wenxiang Chen, Boyang Hong +18
In this paper, we propose R: Learning Reasoning through Reverse Curriculum Reinforcement Learning (RL), a novel method that employs only outcome supervision to achieve the bene…
cs.CL2024★ 1 cited
ReFT: Reasoning with Reinforced Fine-Tuning
Trung Quoc Luong, Xinbo Zhang, Zhanming Jie +3
One way to enhance the reasoning capability of Large Language Models (LLMs) is to conduct Supervised Fine-Tuning (SFT) using Chain-of-Thought (CoT) annotations. This approach does…