Zhenwen Liang

DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

Vision-SR1: Self-Rewarding Vision-Language Model via Reasoning Decomposition and Multi-Reward Policy Optimization

On the Evolution of Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Emergent Deceptive Behaviors in Reward-Optimizing LLMs

DeepTheorem: Advancing LLM Reasoning for Theorem Proving Through Natural Language and Reinforcement Learning

CLUE: Non-parametric Verification from Experience via Hidden-State Clustering

VOGUE: Guiding Exploration with Visual Uncertainty Improves Multimodal Reasoning

MPS-Prover: Advancing Stepwise Theorem Proving by Multi-Perspective Search and Data Curation

Learning Molecular Representation in a Cell

Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification