1 citations · 1 across the 4 of their papers we have counts for
9 papers
HiPRAG: Hierarchical Process Rewards for Efficient Agentic Retrieval Augmented Generation
Peilin Wu, Mian Zhang, Kun Wan +4
Agentic RAG is a powerful technique for incorporating external information that LLMs lack, enabling better problem solving and question answering. However, suboptimal search behavi…
GEAR: A General Evaluation Framework for Abductive Reasoning
Kaiyu He, Peilin Wu, Mian Zhang +4
Since the advent of large language models (LLMs), research has focused on instruction following and deductive reasoning. A central question remains: can these models discover new k…
Vision-Zero: Scalable VLM Self-Improvement via Strategic Gamified Self-Play
Qinsi Wang, Bo Liu, Tianyi Zhou +6
Although reinforcement learning (RL) has emerged as a promising approach for improving vision-language models (VLMs) and multimodal large language models (MLLMs), current methods r…
EPO: Entropy-regularized Policy Optimization for LLM Agents Reinforcement Learning
Wujiang Xu, Wentian Zhao, Zhenting Wang +6
Training LLM agents in multi-turn environments with sparse rewards, where completing a single task requires 30+ turns of interaction within an episode, presents a fundamental chall…
Understanding and Mitigating Numerical Sources of Nondeterminism in LLM Inference
Jiayi Yuan, Hao Li, Xinheng Ding +7
Large Language Models (LLMs) are now integral across various domains and have demonstrated impressive performance. Progress, however, rests on the premise that benchmark scores are…
DUMP: Automated Distribution-Level Curriculum Learning for RL-based LLM Post-training
Zhenting Wang, Guofeng Cui, Yu-Jhe Li +2
Recent advances in reinforcement learning (RL)-based post-training have led to notable improvements in large language models (LLMs), particularly in enhancing their reasoning capab…