arXiv:2605.27736v1 Announce Type: new Abstract: Online reinforcement learning is becoming increasingly important for aligning diffusion models with non-differentiable objectives. However, existing methods still face limitations in assigning fine-grained credit along denoising trajectories and in realizing stable value-based optimization. We propose a state-aligned latent actor-critic framework for diffusion post-training, in which the diffusion model serves as its own timestep-conditioned value function and predicts values directly on noisy latent states. This enables trajectory-level PPO trai
The paper addresses current limitations in aligning diffusion models with non-differentiable objectives, a critical bottleneck in advanced AI development, demonstrating an active area of research pushing the boundaries of AI capabilities.
Improved alignment for diffusion models makes them more controllable and predictable, which is essential for deploying them in sensitive or critical applications, from generative AI to robotics.
The proposed state-aligned latent actor-critic framework offers a more stable and fine-grained credit assignment mechanism for training diffusion models, potentially accelerating their reliability and performance.
- · AI researchers
- · Generative AI companies
- · Diffusion model developers
- · Companies relying on less efficient or stable diffusion model alignment techniqu
More robust and controllable diffusion models emerge across various applications.
This leads to faster development and deployment of advanced AI applications, particularly in content generation, drug discovery, and robotics.
The enhanced capability of diffusion models could contribute to the acceleration of general AI agent development by providing more sophisticated world models and action planning.
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Read at arXiv cs.LG