SOURCE-LINKED INTELLIGENCE
ReCAST: Reward Credit Assignment across Timesteps for Online Diffusion Reinforcement
Training diffusion models with multiple rewards requires distinguishing user preference from reward informativeness. User preference determines how much each reward should contribute to the overall objective; reward informativeness determines when its feedback is useful during denoising. Some rewards can meaningfully evaluate a sample as soon as global structure emerges, but others become informative only when the sample is nearly clean. To address both questions jointly, we propose ReCAST (Reward Credit ASsignment across T}imesteps), the first method, to our knowledge, for per-reward, timeste
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-11T18:41:09.000Z
First collected: 2026-09-20T16:41:15.630Z. This is not the publication date.