23 citations · 98 across the 68 of their papers we have counts for
4 papers · 1 filter
Don't Tell the Answer, Truly Guide the Reasoning During RL Rollouts
Xinyi Wang, Jinyi Han, Zishang Jiang +7
Reinforcement Learning (RL) has become a key driver for enhancing the long chain-of-thought (CoT) reasoning capabilities of Large Language Models (LLMs). However, prevalent methods…
CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From Mistakes
Haokun Zhao, Haixia Han, Jie Shi +3
As world knowledge advances and new task schemas emerge, Continual Learning (CL) becomes essential for keeping Large Language Models (LLMs) current and addressing their shortcoming…
Laying the Foundation First? Investigating the Generalization from Atomic Skills to Complex Reasoning Tasks
Yuncheng Huang, Qianyu He, Yipei Xu +2
Current language models have demonstrated their capability to develop basic reasoning, but struggle in more complicated reasoning tasks that require a combination of atomic skills,…
Collective Loss Function for Positive and Unlabeled Learning
Chenhao Xie, Qiao Cheng, Jiaqing Liang +2
People learn to discriminate between classes without explicit exposure to negative examples. On the contrary, traditional machine learning algorithms often rely on negative example…