UniT framework trains unified multimodal models to plan, inspect, and refine iteratively
burkov · x · 2026-11-01
Andriy Burkov summarizes research on inference-time compute for unified multimodal models.
- Problem: unified models that both interpret and create images typically produce results in one pass, limiting reliability on tasks requiring multiple instructions, stacked edits, or verifiable visual conclusions
- Method: UniT trains one unified model to plan, inspect its outputs, and refine them over multiple rounds. The team used other models to generate and critique images, producing 12,000 filtered examples of multi-step reasoning and editing
- Results: they fine-tuned Bagel on this data and tested across image generation, compositional editing, multi-turn editing, and visual understanding tasks
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