Identify additional useful regression-target corrections for diffusion training

Identify other useful corrections that can be written into the regression target of diffusion-model training, beyond the weak-to-strong target extrapolation used by SWIFT.

Background

SWIFT demonstrates that modifying the regression target can improve a diffusion denoiser after finite training, even when the modified target is intentionally biased relative to the posterior mean. The method uses a frozen weak denoiser to extrapolate the clean target away from the weak prediction, thereby transferring a weak-to-strong correction from sampling into training while discarding the guide at inference.

The conclusion frames a broader unresolved direction: treating the regression target as a design interface may permit other correction mechanisms that improve denoiser quality or sampling performance. The paper does not specify what these additional corrections should be or establish their effectiveness.

References

This suggests a broader view of the regression target as a design interface rather than a fixed quantity---and raises the question of what other useful corrections can be written into it.

— SWIFT: Self-guided Weak-to-strong dIFfusion models via Target extrapolation  (2610.25844 - He et al., 19 Oct 2026) in Section Conclusion