Learning Native Reflection in Unified Models with Interleaved Reinforcement Learning

arXiv:2609.35767 · cs.CV, cs.AI · Submitted 2026-09-28 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/waltstephen/UMM-Reflection

Project page: https://waltstephen.github.io/UMM-Reflection

Terminology

• Benchmark — 45× in this paper · explained in 2 episodes
• Verifier — 35× in this paper · explained in 2 episodes
• Accuracy — 23× in this paper · explained in 1 episode
• Alignment — 20× in this paper · explained in 1 episode
• Reinforcement Learning — 15× in this paper · explained in 4 episodes
• GRPO — 12× in this paper · explained in 6 episodes
• Test-time Scaling — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• AUC — 5× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• Consistency — 5× in this paper · explained in 3 episodes
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Iterative Refinement — 3× in this paper · explained in 3 episodes
• Linear probe — 3× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Decoupling — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Best-of-N Sampling — 1× in this paper · explained in 2 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Flow Matching Model — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Introspection — 1× in this paper · explained in 2 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Verifiable Rewards — 1× in this paper · explained in 2 episodes

Sources

• BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
• Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
• Thinking with Generated Images
• Emerging Properties in Unified Multimodal Pretraining
• Iterative Refinement Improves Compositional Image Generation
• UniRL: Self-Improving Unified Multimodal Models via Supervised and Reinforcement Learning
• Transfer between Modalities with MetaQueries
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Towards Enhanced Image Generation Via Multi-modal Chain of Thought in Unified Generative Models
• DanceGRPO: Unleashing GRPO on Visual Generation
• ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL

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