7 citations · 14 across the 15 of their papers we have counts for
6 papers · 1 filter
ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning
Zexi Liu, Yuzhu Cai, Xinyu Zhu +6
As AI capabilities advance toward and potentially beyond human-level performance, a natural transition emerges where AI-driven development becomes more efficient than human-centric…
ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning
Ziyu Wan, Yunxiang Li, Xiaoyu Wen +8
Recent research on Reasoning of Large Language Models (LLMs) has sought to further enhance their performance by integrating meta-thinking -- enabling models to monitor, evaluate, a…
PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning
Kun Hu, Muning Wen, Xihuai Wang +5
Multi-agent reinforcement learning (MARL) faces challenges in coordinating agents due to complex interdependencies within multi-agent systems. Most MARL algorithms use the simultan…
ThinkBench: Dynamic Out-of-Distribution Evaluation for Robust LLM Reasoning
Shulin Huang, Linyi Yang, Yan Song +9
Evaluating large language models (LLMs) poses significant challenges, particularly due to issues of data contamination and the leakage of correct answers. To address these challeng…
Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning
Jiachen Zhu, Congmin Zheng, Jianghao Lin +5
While large language models (LLMs) have significantly advanced mathematical reasoning, Process Reward Models (PRMs) have been developed to evaluate the logical validity of reasonin…
Language Games as the Pathway to Artificial Superhuman Intelligence
Ying Wen, Ziyu Wan, Shao Zhang
The evolution of large language models (LLMs) toward artificial superhuman intelligence (ASI) hinges on data reproduction, a cyclical process in which models generate, curate and r…