most citedInfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning

2 citations · 2 across the 18 of their papers we have counts for

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cs.CL2026

Beyond Captions: Context-Grounded Reconstruction for Biomedical Multimodal Continued Pretraining

Guanghao Zhu, Zeyu Liu, Zhitian Hou +10

Biomedical figures are explained not by captions alone but by body-text passages that discuss them. Yet current multimodal corpora typically reduce figures to isolated image-captio…

cs.CL2025

InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training

Pengkai Wang, Pengwei Liu, Qi Zuo +3

Reinforcement learning (RL) has powered many recent breakthroughs in large language models (LLMs), especially for tasks where rewards can be computed automatically, such as code ge…

cs.CL2025

A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models

Wenjun Wang, Shuo Cai, Congkai Xie +7

The immense computational cost of training Large Language Models (LLMs) presents a major barrier to innovation. While FP8 training offers a promising solution with significant theo…

cs.CL2025

InfiMed: Low-Resource Medical MLLMs with Advancing Understanding and Reasoning

Zeyu Liu, Zhitian Hou, Guanghao Zhu +3

Multimodal Large Language Models (MLLMs) have achieved remarkable progress in domains such as visual understanding and mathematical reasoning. However, their application in the med…

cs.CL20252 cited

InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning

Congkai Xie, Shuo Cai, Wenjun Wang +17

Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have made significant advancements in reasoning capabilities. However, they still face challenges such as…