5 papers
Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective
Zhezheng Hao, Hong Wang, Haoyang Liu +6
Reinforcement Learning with Verifiable Rewards (RLVR) serves as a cornerstone technique for enhancing the reasoning capabilities of Large Language Models (LLMs). However, its train…
ReCreate: Reasoning and Creating Domain Agents Driven by Experience
Zhezheng Hao, Hong Wang, Jian Luo +6
Large Language Model agents are reshaping the industrial landscape. However, most practical agents remain human-designed because tasks differ widely, making them labor-intensive to…
Scheduling Your LLM Reinforcement Learning with Reasoning Trees
Hong Wang, Zhezheng Hao, Jian Luo +6
Using Reinforcement Learning with Verifiable Rewards (RLVR) to optimize Large Language Models (LLMs) can be conceptualized as progressively editing a query's `Reasoning Tree'. This…
Accelerating Eigenvalue Dataset Generation via Chebyshev Subspace Filter
Hong Wang, Jie Wang, Jian Luo +4
Eigenvalue problems are among the most important topics in many scientific disciplines. With the recent surge and development of machine learning, neural eigenvalue methods have at…
STNet: Spectral Transformation Network for Solving Operator Eigenvalue Problem
Hong Wang, Jiang Yixuan, Jie Wang +3
Operator eigenvalue problems play a critical role in various scientific fields and engineering applications, yet numerical methods are hindered by the curse of dimensionality. Rece…