3 papers
cs.CL2026
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…
cs.AI2025
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…
cs.CL2025
Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual Prompts
Teng Wang, Zhenqi He, Wing-Yin Yu +2
With the advent of Large Language Models (LLMs), generating rule-based data for real-world applications has become more accessible. Due to the inherent ambiguity of natural languag…