1 citations · 2 across the 9 of their papers we have counts for
10 papers
Thinking with Visual Grounding
Junkai Zhang, Yihe Deng, Kai-Wei Chang +1
Visual thinking should not only sound right; it should show its evidence. While recent vision-language models (VLMs) can produce natural-language reasoning traces, these traces oft…
OpenVLThinkerV2: A Generalist Multimodal Reasoning Model for Multi-domain Visual Tasks
Wenbo Hu, Xin Chen, Yan Gao-Tian +3
Group Relative Policy Optimization (GRPO) has emerged as the de facto Reinforcement Learning (RL) objective driving recent advancements in Multimodal Large Language Models. However…
Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning
Yihe Deng, I-Hung Hsu, Jun Yan +7
Large Language Models (LLMs) often struggle with problems that require multi-step reasoning. For small-scale open-source models, Reinforcement Learning with Verifiable Rewards (RLV…
More is Less: The Pitfalls of Multi-Model Synthetic Preference Data in DPO Safety Alignment
Yifan Wang, Runjin Chen, Bolian Li +7
Aligning large language models (LLMs) with human values is an increasingly critical step in post-training. Direct Preference Optimization (DPO) has emerged as a simple, yet effecti…
Entropy-Based Adaptive Weighting for Self-Training
Xiaoxuan Wang, Yihe Deng, Mingyu Derek Ma +1
The mathematical problem-solving capabilities of large language models have become a focal point of research, with growing interests in leveraging self-generated reasoning paths as…
OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles
Yihe Deng, Hritik Bansal, Fan Yin +3
We introduce OpenVLThinker, one of the first open-source large vision-language models (LVLMs) to exhibit sophisticated chain-of-thought reasoning, achieving notable performance gai…
Enhancing LLM Safety Through a Theoretical Minimax Game Lens
Yihe Deng, Yu Yang, Junkai Zhang +2
The rapid advancement of large language models (LLMs) necessitates effective mechanisms to ensure their responsible deployment by accurately distinguishing unsafe content from beni…
Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning
Yihe Deng, Paul Mineiro
Mathematical reasoning is a crucial capability for Large Language Models (LLMs), yet generating detailed and accurate reasoning traces remains a significant challenge. This paper i…
MIRAI: Evaluating LLM Agents for Event Forecasting
Chenchen Ye, Ziniu Hu, Yihe Deng +4
Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems.…
Enhancing Large Vision Language Models with Self-Training on Image Comprehension
Yihe Deng, Pan Lu, Fan Yin +6
Large vision language models (LVLMs) integrate large language models (LLMs) with pre-trained vision encoders, thereby activating the perception capability of the model to understan…