8 papers
Latent Sketchpad: Sketching Visual Thoughts to Elicit Multimodal Reasoning in MLLMs
Huanyu Zhang, Wenshan Wu, Chengzu Li +9
While Multimodal Large Language Models (MLLMs) excel at visual understanding, they often struggle in complex scenarios that require visual planning and imagination. Inspired by how…
BaseReward: A Strong Baseline for Multimodal Reward Model
Yi-Fan Zhang, Haihua Yang, Huanyu Zhang +11
The rapid advancement of Multimodal Large Language Models (MLLMs) has made aligning them with human preferences a critical challenge. Reward Models (RMs) are a core technology for…
Debiasing Multimodal Large Language Models via Penalization of Language Priors
YiFan Zhang, Yang Shi, Weichen Yu +6
In the realms of computer vision and natural language processing, Multimodal Large Language Models (MLLMs) have become indispensable tools, proficient in generating textual respons…
R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning
Yi-Fan Zhang, Xingyu Lu, Xiao Hu +13
Multimodal Reward Models (MRMs) play a crucial role in enhancing the performance of Multimodal Large Language Models (MLLMs). While recent advancements have primarily focused on im…
MME-Unify: A Comprehensive Benchmark for Unified Multimodal Understanding and Generation Models
Wulin Xie, Yi-Fan Zhang, Chaoyou Fu +6
Existing MLLM benchmarks face significant challenges in evaluating Unified MLLMs (U-MLLMs) due to: 1) lack of standardized benchmarks for traditional tasks, leading to inconsistent…
MM-RLHF: The Next Step Forward in Multimodal LLM Alignment
Yi-Fan Zhang, Tao Yu, Haochen Tian +17
Despite notable advancements in Multimodal Large Language Models (MLLMs), most state-of-the-art models have not undergone thorough alignment with human preferences. This gap exists…