CoCA redistributes a single final image reward across denoising steps using cosine similarity between intermediate and final latents, improving RL fine-tuning sample efficiency on four human preference rewards.
Learning transferable visual models from natural language supervision,
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Step-level Reward for Free in RL-based T2I Diffusion Model Fine-tuning
CoCA redistributes a single final image reward across denoising steps using cosine similarity between intermediate and final latents, improving RL fine-tuning sample efficiency on four human preference rewards.