LaViDa-R1: Advancing Reasoning for Unified Multimodal Diffusion Language Models

• URL: http://arxiv.org/abs/2602.14147v1
• Date: Sun, 15 Feb 2026 13:52:45 GMT
• Title: LaViDa-R1: Advancing Reasoning for Unified Multimodal Diffusion Language Models
• Abstract summary: We propose LaViDa-R1, a multimodal, general-purpose reasoning dLLM.<n>LaViDa-R1 incorporates diverse multimodal understanding and generation tasks in a unified manner.<n>It employs several novel training techniques, including answer-forcing, tree search, and complementary likelihood estimation.
• Score: 86.78753308621462
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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