Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View
Reinforcement learning (RL) post-training provides a direct way to align diffusion models with human preferences and task-specific rewards. However, current RL algorithms for diffusion models remain fragmented: reverse-trajectory methods rely on discretized likelihood ratios, whereas forward-matching methods train on reward-labeled noising versions of the rollout samples. This paper shows that these seemingly different losses arise from a single path-space principle. Starting from the regularized diffusion-RL objective, we use importance sampling between sampling SDEs to obtain an explicit pol
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-xl-base-1.0 →
“Fuzzy title match (0.73): “Designing Reinforcement Learning for Diffusion Models: A Uni” ≈ “stabilityai/stable-diffusion-xl-base-1.0””
- FuzzySimilar title/name (fuzzy) · 59%CompVis/stable-diffusion-v1-4 →
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-3.5-large →
“Fuzzy title match (0.73): “Designing Reinforcement Learning for Diffusion Models: A Uni” ≈ “stabilityai/stable-diffusion-3.5-large””
- PossiblePossibly related (embedding) · 50%Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment →
- PossiblePossibly related (embedding) · 50%RL without TD learning →
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