Latent Reward Registers for Diffusion Preference Alignment

Aligning diffusion models with human preferences usually relies on a sparse terminal reward evaluated on the final generated samples, presenting a severe temporal credit-assignment challenge across the multi-step denoising process. We propose Latent Reward Registers, a mechanism that estimates terminal preference directly from intermediate noisy latents by prepending learnable, position-free register tokens to the input sequence of a frozen Diffusion Transformer (DiT). This independent readout m

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ethics.ai (4 August 2026), “Latent Reward Registers for Diffusion Preference Alignment,” evidence record 16574, https://ethics.ai/record/16574 (originally published by arXiv cs.LG).

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