Recursive Self-Improvement in Unified Multimodal Models
Unified multimodal models (UMMs) understand and generate both text and images, which lets a model produce its own training data. Existing self-improvement in UMMs keeps supervision on the visual side, where image understanding judges image generation. We propose recursive cross-capability self-improvement (RSI), a training loop in which the text and visual abilities of a UMM supply training data for one another. In each round, the model generates images and reads them to find where it falls short. It then writes programs aimed at these shortcomings, and execution verifies every result against its specification. Verified renders train image generation, while labeled renders and the model's...
| Authors | Huijuan Wang, Chufan Shi, Cheng Yang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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