7 papers
Proof-RM: A Scalable and Generalizable Reward Model for Math Proof
Haotong Yang, Zitong Wang, Shijia Kang +7
While Large Language Models (LLMs) have demonstrated strong math reasoning abilities through Reinforcement Learning with *Verifiable Rewards* (RLVR), many advanced mathematical pro…
GREPO: A Benchmark for Graph Neural Networks on Repository-Level Bug Localization
Juntong Wang, Libin Chen, Xiyuan Wang +4
Repository-level bug localization-the task of identifying where code must be modified to fix a bug-is a critical software engineering challenge. Standard Large Language Modles (LLM…
The Road Less Traveled: Enhancing Exploration in LLMs via Sequential Sampling
Shijia Kang, Muhan Zhang
Reinforcement learning (RL) has been pivotal in enhancing the reasoning capabilities of large language models (LLMs), but it often suffers from limited exploration and entropy coll…
On the Completeness of Invariant Geometric Deep Learning Models
Zian Li, Xiyuan Wang, Shijia Kang +1
Invariant models, one important class of geometric deep learning models, are capable of generating meaningful geometric representations by leveraging informative geometric features…
Beyond Single-Task: Robust Multi-Task Length Generalization for LLMs
Yi Hu, Shijia Kang, Haotong Yang +2
Length generalization, the ability to solve problems longer than those seen during training, remains a critical challenge for large language models (LLMs). Previous work modifies p…
Number Cookbook: Number Understanding of Language Models and How to Improve It
Haotong Yang, Yi Hu, Shijia Kang +2
Large language models (LLMs) can solve an increasing number of complex reasoning tasks while making surprising mistakes in basic numerical understanding and processing (such as 9.1…