UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models
arXiv:2604.18518v3 Announce Type: replace-cross Abstract: Uniform Discrete Diffusion Model (UDM) has recently emerged as a promising paradigm for discrete generative modeling; however, its integration with reinforcement learning remains largely unexplored. We observe that naively applying GRPO to UDM leads to training instability and marginal performance gains. To address this, we propose UDM-GRPO, the first framework to integrate UDM with RL. Our method is guided by two key insights: (i) treating the final clean sample as the action provides more accurate and stable optimization signals; and
The rapid advancement in discrete generative modeling, specifically Uniform Discrete Diffusion Models (UDMs), is now being explored for direct integration with reinforcement learning to improve stability and efficiency in AI agent training.
This breakthrough represents a significant step towards more stable and effective reinforcement learning for complex discrete generative tasks, potentially leading to more robust and capable AI systems.
The ability to stably integrate UDMs with Reinforcement Learning (RL) through UDM-GRPO provides a new and more efficient optimization pathway for discrete generative AI, moving beyond prior limitations.
- · AI researchers
- · Generative AI developers
- · Robotics
- · Autonomous system developers
- · Traditional RL methods for discrete generative modeling
- · Inefficient AI training approaches
Improved performance and stability in training discrete generative AI models using reinforcement learning.
Acceleration in the development of more sophisticated AI agents capable of complex decision-making and generation tasks.
These advancements could make AI agents more pervasive in applications requiring high-fidelity discrete outputs, such as advanced manufacturing or drug discovery.
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Read at arXiv cs.LG