1 citations · 1 across the 5 of their papers we have counts for
4 papers · 1 filter
Rethinking Reasoning Quality in Large Language Models through Enhanced Chain-of-Thought via RL
Haoyang He, Zihua Rong, Kun Ji +5
Reinforcement learning (RL) has recently become the dominant paradigm for strengthening the reasoning abilities of large language models (LLMs). Yet the rule-based reward functions…
We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning
Runqi Qiao, Qiuna Tan, Peiqing Yang +11
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various tasks, but still struggle with complex mathematical reasoning. Existing research p…
Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models
YiFan Zhang, Shanglin Lei, Runqi Qiao +10
The rapidly developing field of large multimodal models (LMMs) has led to the emergence of diverse models with remarkable capabilities. However, existing benchmarks fail to compreh…
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?
Runqi Qiao, Qiuna Tan, Guanting Dong +15
Visual mathematical reasoning, as a fundamental visual reasoning ability, has received widespread attention from the Large Multimodal Models (LMMs) community. Existing benchmarks,…