VeRL-Omni

Easy, fast, and stable RL training for diffusion and omni-modality models

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VeRL-Omni is a general RL training framework focused on multimodal generative models, built on top of verl.

It originated from the multi-modal generation RL effort in verl, and now has a dedicated home so it can evolve in a more focused way.

News 🔥

• [2026-10] 🎉 We have released v0.3.0 for fully async diffusion/omni RL with the v1 trainer as the default.
• [2026-10] OmniNFT is now supported for audio-video post-training. The LTX2.3 text-to-video+audio LoRA recipe runs on Ascend NPU with modality-wise advantage routing.
• [2026-09] The v1 trainer (TransferQueue + ReplayBuffer) is now the default for diffusion models; the legacy v0 trainer is deprecated, while offline DPO remains on v0 by design.
• [2026-08] DiffusionOPD (on-policy distillation, including multi-teacher MOPD) is now supported.
• [2026-08] 🔥 MiniMax-H3 now supports T2VA, FL2VA, and Ref2VA with both FlowGRPO and DiffusionNFT.
• [2026-08] 🎉 We have released v0.2.0 for faster diffusion rl and more stable Qwen3-Omni multimodal training. Blog: VeRL-Omni v0.2.0
• [2026-08] LTX2.3 text-to-video+audio model is now supported with FlowGRPO.
• [2026-07] Team-proposed algorithm FlowGRPO with DiNa-LRM is available. Training skips VAE decoding by scoring clean diffusion latents directly for faster and more resource-efficient model alignment.
• [2026-07] VeRL-Omni is presented in QingKe AI, vLLM community, and verl x Ascend Beijing meetup. Slides are shared.
• [2026-06] Qwen3-Omni GSPO Trainer is available! Flow-DPPO is integrated. vLLM-Omni rollout backend is upgraded to v0.22 for higher throughput, with default actor attn backend switched to FA3.
• [2026-06] DiffusionNFT and Diffusion DPO are integrated with verified recipes on Qwen-Image/SD3.5. Wan2.2 is now supported for video generation tasks.

Why VeRL-Omni

Multimodal generative RL training differs from text-only LLM RL not only in model structure, but also in I/O patterns, compute characteristics, and runtime bottlenecks. As this space grows, it deserves a dedicated training repository that can evolve quickly around its own constraints.

Scope

VeRL-Omni targets RL post-training for three families of generative models:

1. Diffusion generative models for image, video, and audio — e.g., Qwen-Image, Wan2.2.
2. Unified multimodal understanding + generation models — e.g., BAGEL, HunyuanImage-3.0.
3. Omni-modality models that jointly handle text, image, audio, and video — e.g., Qwen3-Omni.

What we focus on

• Fast multi-modal rollout: Adopt vLLM-Omni backend and accelerate generation via rollout routing, rollout batching, embed caching optimizations, and more.
• Flexible & async multi-reward serving: Support multi-reward serving (HPSv3, GenRM-OCR, UnifiedReward, etc.), HTTP scorer, and asynchronous reward computation to overlap the rollout phase.
• Modular training backends: Selectable VeOmni and FSDP2 backends with combinable parallelism (USP/TP/DP) for distributed training.
• Stability: Boost stability and speed in diffusion RL pipelines via rollout correction to skip logP recomputation, and achieve reproducible E2E training with deterministic RL. Reward, rollout and actor update are composable and extensible, via Hydra configs.
• Efficient and convergent training recipes: On our reference Qwen-Image FlowGRPO setup, VeRL-Omni achieves ~25% higher end-to-end throughput than the diffusers-based flow_grpo implementation, driven by vLLM-Omni rollout, FSDP2 trainer, overlapped reward computation (asynchronous), etc.

Getting Started 🚀

Visit our documentation to learn more.

• Installation
• Quickstart

Model and Algorithm Support 🎨

Ascend NPU Support 💠

VeRL-Omni supports Ascend A2, A3, and A5 NPUs. See the NPU installation guide for setup and the Ascend NPU Quickstart Guide for FlowGRPO training.

Roadmap 🗺

Future work is tracked in VeRL-Omni Q4 Roadmap

Contributing 🤝

Contributions are welcome.

See the contribution guide.

Join the Community: Feel free to ask questions, provide feedback, and discuss with fellow users of VeRL-Omni in our WeChat group.

Acknowledgement 🌟

verl-omni builds on the engineering foundations developed in verl and is closely aligned with multimodal inference systems such as vLLM-Omni.

Citation 📚

If you find the project helpful, please cite and star ⭐

@misc{verlomni_github,
  title        = {{VeRL-Omni: Easy, Fast, and Stable RL Training for Diffusion and Omni-Modality Models}},
  author       = {Yongxiang Huang and Cheung Kawai and Jingan Zhou and Yingshu Chen and {openYuanrong Team} and Xibin Wu},
  year         = {2026},
  howpublished = {\url{https://github.com/verl-project/verl-omni}},
  urldate      = {2026-04-28}
}