DFR Framework Tackles Reward Fine-Tuning in Diffusion Models Without Losing Diversity
mmbronstein · x · 2026-10-28
A new paper introduces DFR (Distributional Functional Rewards), addressing a core tension in reward fine-tuning for diffusion and flow models: chasing high rewards typically sacrifices sample diversity or drifts far from the pretrained distribution.
DFR balances reward optimization with preserving desirable properties of the generated distribution, with a thread expanding on the details.
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