On Oracle Design for Guided Diffusion in the Low-Data Regime

In this work, we propose to use the reverse dynamics of diffusion models to train oracles suited for steering in the low-data regime. Steering diffusion models toward desired properties at inference time requires oracles that properly map intermediate noised states to end-state properties. In the scarce labelled data regime, however, learning such a mapping is challenging. Crucially, the reverse process dynamics carry valuable learning signal on how predictions should evolve across timesteps, one that can be leveraged without any labels. We exploit this by training oracles in the original data space while constraining their predictions on consecutive intermediate noised states to adhere to reverse process dynamics, requiring no additional labelling. Optimizing this objective is non-trivial (its loss nests an expectation over the stochastic reverse dynamics), for which we develop an anchored-target scheme to facilitate a principled optimization. In a systematic benchmark of thousands of experimental configurations, spanning regression and classification tasks in both continuous and discrete design spaces, our approach achieves superior steering quality across varied labelled data availability. These results establish dynamics-aware oracle training as the right strategy in the label-scarce regime, broadly applicable across diffusion and flow-based models. Code is available at https://anonymous.4open.science/r/code_dapp-0F53/.