SenseNova U1.5-Lite full release: expert training, OPD distillation, one model at inference

Benchmarks:

Instead of just scaling up, they train task-specialized expert models for text rendering and infographics, aesthetic quality, and image editing. Then OPD consolidates those experts back into U1.5-Lite. At inference you get one model. No router, no expert switching, no manual selection. The slogan they use: specialized in training, unified in delivery.

Post-training now includes task-oriented RL with three user-visible objectives: instruction adherence, visual quality and preference alignment, and edit fidelity with preservation of untouched regions.

What improved:

• Complex instruction following. Multiple constraints in one request (subjects, counts, spatial relationships, text, layout, style, preservation requirements) handled more consistently.
• Text rendering and dense layouts. Chinese and English in posters and infographics.
• Native 4K generation with stable global structure.
• Native editing with better preservation of subject identity, geometry, and unedited regions. Multi-reference editing and precise local edits included.
• Visual understanding strengthens generation. Representations learned from understanding tasks (object relations, spatial structure, layout, info hierarchy) transfer to generation and editing. JSON-structured supervision is used as a training mechanism for controllability, not a mandatory user-facing format. Natural language stays the default interface.

Links:
GitHub: https://github.com/OpenSenseNova/SenseNova-U1
HuggingFace: https://huggingface.co/sensenova/SenseNova-U1.5-8B-MoT