LARC 框架实现连续空间潜在推理以统一多模态模型
Latent Reasoning in Continuous Space for Unified Multimodal Models
LARC 框架通过在连续隐藏状态上进行推理，缓解了统一多模态模型在图像生成中应用语言推理的瓶颈，提升了生成质量并实现无离散标记瓶颈的测试时扩展。该框架包含课程监督微调（SFT）和信息增益强化学习（RL）两个阶段，在文本到图像生成基准测试中，LARC 在 GenEval 上比 BAGEL 提升了 7% 的性能，并展现出明确的测试时扩展行为。
Published on Oct 30
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Abstract
LARC enables continuous latent reasoning for unified multimodal models, improving image generation quality and enabling test-time scaling without discrete token bottlenecks.
Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation tasks, yet they still struggle to effectively leverage language-based chain-of-thought reasoning for image generation. As a result, existing reasoning-augmented UMMs often fail to inherit the test-time scaling and fine-grained controllability observed in large language models. We attribute this failure to a fundamental modality mismatch: reasoning is performed in a discrete token space, whereas image generation operates in a continuous space. To bridge this gap, we introduce a Latent Reasoning framework in Continuous space (LARC), which alleviates the token-level bottleneck by reasoning over continuous hidden states. Specifically, the training framework consists of two stages: (i) curriculum supervised fine-tuning (SFT), and (ii) information-gain reinforcement learning (RL). First, the curriculum SFT stage gradually converts token-level reasoning into latent reasoning. Subsequently, the information-gain RL stage self-evolves latent reasoning without ground truth supervision. Experiments across diverse text-to-image generation benchmarks show that LARC consistently improves generation quality, prompt following, and compositional fidelity. Notably, LARC achieves state-of-the-art performance on GenEval with a 7%p gain over BAGEL and demonstrates clear test-time scaling behavior, outperforming token-based generation under both parallel and sequential test-time scaling strategies.
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