Explicit Critic Guidance for Aligning Diffusion Models

• URL: http://arxiv.org/abs/2605.27736v1
• Date: Tue, 26 May 2026 22:20:51 GMT
• Title: Explicit Critic Guidance for Aligning Diffusion Models
• Abstract summary: We propose a state-aligned latent actor-critic framework for diffusion post-training.<n>Our method consistently outperforms prior group-relative RL and actor-critic baselines on single-reward and multi-reward benchmarks.
• Score: 27.6704888666835
• License: http://creativecommons.org/licenses/by/4.0/
• 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 training, supports stable actor-critic optimization with simple conditioning and value pretraining strategies, and naturally allows the learned critic to be reused for inference-time steering. We further extend the framework to multi-reward optimization, where joint training with complementary rewards helps alleviate reward hacking. Across both UNet- and DiT-based backbones, our method consistently outperforms prior group-relative RL and actor-critic baselines on single-reward and multi-reward benchmarks, while test-time steering provides additional gains in generation quality.

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