3 citations · 5 across the 6 of their papers we have counts for
6 papers
Pointing to a Llama and Call it a Camel: On the Sycophancy of Multimodal Large Language Models
Renjie Pi, Kehao Miao, Li Peihang +4
Multimodal large language models (MLLMs) have demonstrated extraordinary capabilities in conducting conversations based on image inputs. However, we observe that MLLMs exhibit a pr…
Fake it till You Make it: Reward Modeling as Discriminative Prediction
Runtao Liu, Jiahao Zhan, Yingqing He +3
An effective reward model plays a pivotal role in reinforcement learning for post-training enhancement of visual generative models. However, current approaches of reward modeling s…
VL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training
Jipeng Zhang, Kehao Miao, Renjie Pi +4
Reinforcement Fine-Tuning (RFT) with verifiable rewards has advanced large language models but remains underexplored for Vision-Language (VL) models. The Vision-Language Reward Mod…
VideoDPO: Omni-Preference Alignment for Video Diffusion Generation
Runtao Liu, Haoyu Wu, Zheng Ziqiang +4
Recent progress in generative diffusion models has greatly advanced text-to-video generation. While text-to-video models trained on large-scale, diverse datasets can produce varied…
LLMs Meet Multimodal Generation and Editing: A Survey
Yingqing He, Zhaoyang Liu, Jingye Chen +13
With the recent advancement in large language models (LLMs), there is a growing interest in combining LLMs with multimodal learning. Previous surveys of multimodal large language m…
Strengthening Multimodal Large Language Model with Bootstrapped Preference Optimization
Renjie Pi, Tianyang Han, Wei Xiong +4
Multimodal Large Language Models (MLLMs) excel in generating responses based on visual inputs. However, they often suffer from a bias towards generating responses similar to their…