LaViDa-R1: Advancing Reasoning for Unified Multimodal Diffusion Language Models
Diffusion language models (dLLMs) recently emerged as a promising alternative to auto-regressive LLMs. The latest works further extended it to multimodal understanding and generation tasks. In this work, we propose LaViDa-R1, a multimodal, general-purpose reasoning dLLM. Unlike existing works that build reasoning dLLMs through task-specific reinforcement learning, LaViDa-R1 incorporates diverse multimodal understanding and generation tasks in a unified manner. In particular, LaViDa-R1 is built with a novel unified post-training framework that seamlessly integrates supervised finetuning (SFT) and multi-task reinforcement learning (RL). It employs several novel training techniques, including answer-forcing, tree search, and complementary likelihood estimation, to enhance effectiveness and scalability. Extensive experiments demonstrate LaViDa-R1's strong performance on a wide range of multimodal tasks, including visual math reasoning, reason-intensive grounding, and image editing.
6993e08850fb2c0be4783d1a
Shufan Li
6993e08850fb2c0be4783d1b
Yuchen Zhu
6993e08850fb2c0be4783d1c
Jiuxiang Gu
6993e08850fb2c0be4783d1d
Kangning Liu
6993e08850fb2c0be4783d1e
Zhe Lin
6993e08850fb2c0be4783d1f
Yongxin Chen
6993e08850fb2c0be4783d20
Molei Tao
6993e08850fb2c0be4783d21
Aditya Grover
6993e08850fb2c0be4783d22
Jason Kuen
LaViDa-R1: Advancing Reasoning for Unified Multimodal Diffusion Language Models
Diffusion language models (dLLMs) recently emerged as a promising alternative to auto-regressive LLMs. The latest works further extended it to multimodal understanding and generation tasks. In this work, we propose LaViDa-R1, a multimodal, general-purpose reasoning dLLM. Unlike existing works that build reasoning dLLMs through task-specific reinforcement learning, LaViDa-R1 incorporates diverse multimodal understanding and generation tasks in a unified manner. In particular, LaViDa-R1 is built with a novel unified post-training framework that seamlessly integrates supervised finetuning (SFT) and multi-task reinforcement learning (RL). It employs several novel training techniques, including answer-forcing, tree search, and complementary likelihood estimation, to enhance effectiveness and scalability. Extensive experiments demonstrate LaViDa-R1's strong performance on a wide range of multimodal tasks, including visual math reasoning, reason-intensive grounding, and image editing.
LaViDa-R1 is a multimodal reasoning diffusion language model that unifies supervised fine-tuning and multi-task reinforcement learning with novel training techniques for enhanced performance across visual reasoning and generation tasks.
diffusion language models multimodal understanding multimodal generation unified post-training framework supervised fine-tuning multi-task reinforcement learning answer-forcing tree search complementary likelihood estimation visual math reasoning reason-intensive grounding image editing