STAR: STacked AutoRegressive Scheme for Unified Multimodal Learning

• URL: http://arxiv.org/abs/2512.13752v1
• Date: Mon, 15 Dec 2025 07:02:59 GMT
• Title: STAR: STacked AutoRegressive Scheme for Unified Multimodal Learning
• Authors: Jie Qin, Jiancheng Huang, Limeng Qiao, Lin Ma,
• Abstract summary: We introduce STAR: a STacked AutoRegressive scheme for task-progressive unified multimodal learning.<n>This approach decomposes multimodal learning into multiple stages: understanding, generation, and editing.<n> Experiments demonstrate that STAR achieves state-of-the-art performance on GenEval (0.91), DPG-Bench (87.44), and ImgEdit (4.34)
• Score: 37.68078190711403
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multimodal large language models (MLLMs) play a pivotal role in advancing the quest for general artificial intelligence. However, achieving unified target for multimodal understanding and generation remains challenging due to optimization conflicts and performance trade-offs. To effectively enhance generative performance while preserving existing comprehension capabilities, we introduce STAR: a STacked AutoRegressive scheme for task-progressive unified multimodal learning. This approach decomposes multimodal learning into multiple stages: understanding, generation, and editing. By freezing the parameters of the fundamental autoregressive (AR) model and progressively stacking isomorphic AR modules, it avoids cross-task interference while expanding the model's capabilities. Concurrently, we introduce a high-capacity VQ to enhance the granularity of image representations and employ an implicit reasoning mechanism to improve generation quality under complex conditions. Experiments demonstrate that STAR achieves state-of-the-art performance on GenEval (0.91), DPG-Bench (87.44), and ImgEdit (4.34), validating its efficacy for unified multimodal learning.

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