3 papers
cs.CL2025
Beyond Multiple Choice: Verifiable OpenQA for Robust Vision-Language RFT
Yesheng Liu, Hao Li, Haiyu Xu +9
Multiple-choice question answering (MCQA) has been a popular format for evaluating and reinforcement fine-tuning (RFT) of modern multimodal language models. Its constrained output…
cs.CL2025
FlagEval Findings Report: A Preliminary Evaluation of Large Reasoning Models on Automatically Verifiable Textual and Visual Questions
Bowen Qin, Chen Yue, Fang Yin +26
We conduct a moderate-scale contamination-free (to some extent) evaluation of current large reasoning models (LRMs) with some preliminary findings. We also release ROME, our evalua…
cs.CV2025
FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation
Zheqi He, Yesheng Liu, Jing-shu Zheng +5
We present FlagEvalMM, an open-source evaluation framework designed to comprehensively assess multimodal models across a diverse range of vision-language understanding and generati…