Accelerating Disaggregated RL for Visual Generative LLMs with Diffusion-Based Parallelism and Trainer-Assisted Generation
arXiv:2606.24369v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a dominant post-training paradigm, driving the emergence of high-performance RL systems such as veRL for autoregressive large language models (LLMs). In parallel, diffusion-oriented RL algorithms, e.g., DanceGRPO and FlowGRPO, have rapidly expanded the scope of RL from language reasoning to diffusion-based visual and flow-based generation. However, efficient RL systems for diffusion generative LLMs remain underexplored. Existing implementations, e.g., veRL-Omni, still rely on colocated execution, which simpl
This paper addresses the growing need for more efficient and scalable reinforcement learning systems as AI models become increasingly complex, especially those incorporating visual and generative capabilities.
Efficient RL systems are crucial for developing advanced AI, and this research indicates a significant step towards practical, high-performance training for visual generative models, potentially accelerating their adoption and capabilities.
The shift from colocated to disaggregated execution with diffusion-based parallelism will enable significantly faster and more scalable training of complex visual generative LLMs.
- · AI compute providers
- · Developers of visual generative AI
- · High-performance computing sector
- · Inefficient RL system architectures
- · Companies reliant on older, monolithic AI training approaches
Faster training leads to quicker iteration and development cycles for visual generative LLMs.
The proliferation of more sophisticated visual generative AI models could transform creative industries and content generation.
Enhanced visual AI capabilities may accelerate progress in areas like robotics and simulations, driving demand for even more advanced compute infrastructure.
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Read at arXiv cs.AI