Constructing and Interpreting Digital Twin Representations for Visual Reasoning via Large Language Models and Reinforcement Learning

Visual reasoning may require models to interpret images/videosand respond to implicit text queries across diverse output formats, frompixel-level segmentation masks to natural language descriptions. Exist-ing approaches rely on supervised fine-tuning with task-specific architec-tures. For example, reasoning segmentation, grounding, summarization,and visual question answering each demand distinct model designs andtraining, preventing unified solutions and limiting cross-task/-modalitygeneralization. Hence, we propose DT-R1, a reinforcement learning frame-work that trains large language models (LLMs) to construct digital twin(DT) representations of complex multi-modal visual inputs and then rea-son over these high-level representations as a unified approach to visualreasoning. Specifically, we train DT-R1 using GRPO with a novel rewardthat validates both structural integrity and output accuracy. Evaluationsin six visual reasoning benchmarks, covering two modalities and four tasktypes, demonstrate that DT-R1 consistently achieves improvements overstate-of-the-art task-specific models. DT-R1 opens a new direction wherevisual reasoning emerges from reinforcement learning on with DT repre-sentations.