UniRL runs one RL loop across diffusion, LLM and unified models

🚀Introducing UniRL, an RL infra for unified multimodal models. Together with two new RL algorithms: DRPO and Flow-DPPO. One RL loop across diffusion/flow matching models, LLMs/VLMs, and unified multimodal models👇 Code: https://t.co/fhKEqqFpc8 (yes — U(you)-ni-(need) RL 😉)

1、Most RL stacks are built for one modality. UniRL applies a single post-training loop — generate → score → advantage → update → sync — across model families. Model and algorithm are two independent axes, so your coverage is the model × algorithm product, not a fixed recipe menu. 2、One loop, every modality: text→image, text/image→video, vision-language, text-only LLM and VLM, the LLM→diffusion prompt-enhancer, and unified autoregressive+diffusion generation (Hunyuan-Image 3 and Bagel) — a model class no single-purpose RL repo can even express. 3、Built to scale: pluggable rollout engines (train-side / SGLang / vLLM-Omni) behind one typed contract, FSDP2 sharding, and three deployment modes from a single config knob. 4、Two team-original algorithms headline the release: FlowDPPO: Policy optimization for flow/diffusion models with trust-region masks based on exact divergence (See our paper: Flow-DPPO: Divergence Proximal Policy Optimization for Flow Matching Models https://t.co/7TPFT72RDa) DRPO: LLM RL with a smooth, advantage-weighted quadratic regularizer (See our paper: Rethinking the Divergence Regularization in LLM RL [https://t.co/eXTnK1sCts])