arXiv Machine Learning November 2, 2026 By Byungwoo Jeon, Yoonwoo Jeong, Hyunseok Lee, Minsu Cho, Jinwoo Shin

Latent Reasoning in Continuous Space for Unified Multimodal Models

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The paper introduces Latent Reasoning in Continuous space (LARC), a framework that replaces discrete token-based reasoning with reasoning over continuous hidden states to better align with image generation’s continuous nature. LARC is trained in two stages: curriculum supervised fine‑tuning that gradually shifts from token to latent reasoning, followed by an information‑gain reinforcement learning stage that refines latent reasoning without ground‑truth supervision. Experiments on multiple text‑to‑image benchmarks show that LARC improves generation quality, prompt adherence, and compositional fidelity, achieving state‑of‑the‑art results on GenEval and demonstrating robust test‑time scaling.

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