LUT: Latent Utility Training for Visual Reasoning
Multimodal large language models have advanced visual understanding, yet perception-intensive reasoning remains challenging. Recent latent visual reasoning methods introduce hidden-space computation before answering, but they often rely on costly intermediate supervision, such as bounding boxes, sketches, or interleaved rationales. These strategies focus on how latent states should be shaped, but do not explicitly assess whether the latent is useful for the final answer. We propose LUT, a latent reasoning framework trained with only standard VQA pairs. LUT centers training on Latent Utility at two levels. At the trajectory level, we propose Utility-Aware Latent Distillation SFT, which exp...
| Authors | Jiaxuan Kang, Siyu Chen, Mingda Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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