5 papers
Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models
Teng Wang, Zhangyi Jiang, Zhenqi He +9
Recent studies show that Large Language Models (LLMs) achieve strong reasoning capabilities through supervised fine-tuning or reinforcement learning. However, a key approach, the P…
Generalizable End-to-End Tool-Use RL with Synthetic CodeGym
Weihua Du, Hailei Gong, Zhan Ling +7
Tool-augmented large language models (LLMs), hereafter LLM agents, leverage external tools to solve diverse tasks and interface with the real world. However, current training pract…
SAGE: Strategy-Adaptive Generation Engine for Query Rewriting
Teng Wang, Hailei Gong, Changwang Zhang +1
Query rewriting is pivotal for enhancing dense retrieval, yet current methods demand large-scale supervised data or suffer from inefficient reinforcement learning (RL) exploration.…
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving
Teng Wang, Wing-Yin Yu, Zhenqi He +8
LLMs exhibit advanced reasoning capabilities, offering the potential to transform natural language questions into mathematical models. However, existing open-source datasets in ope…
Decision Information Meets Large Language Models: The Future of Explainable Operations Research
Yansen Zhang, Qingcan Kang, Wing Yin Yu +5
Operations Research (OR) is vital for decision-making in many industries. While recent OR methods have seen significant improvements in automation and efficiency through integratin…