verl-project/verl-omni

VeRL-Omni is a reinforcement-learning post-training framework for diffusion, unified multimodal, and omni-modality generative models. Its principal workflow prepares training data, generates model rollouts, scores outputs with rewards, and updates actors through distributed trainers; diffusion also supports teacher scoring and rollout correction. Model-specific pipeline adapters connect common training and rollout machinery to supported models and algorithms. The README documents asynchronous rewards and selectable training backends. Sampled excerpts are partial, so the graph shows documented orchestration and only source-supported internal wiring, not every model-specific route.