Trees to Flows and Back: Unifying Decision Trees and Diffusion Models

Sai Niranjan, Suvrit Sra

Trees to Flows and Back: Unifying Decision Trees and Diffusion Models: 7 upvotes on Hugging Face Daily Papers, #13 of 21 papers on 2026-05-04. Day-by-day upvote history.

Decision trees and diffusion models are ostensibly disparate model classes, one discrete and hierarchical, the other continuous and dynamic. This work unifies the two by establishing a crisp mathematical correspondence between hierarchical decision trees and diffusion processes in appropriate limiting regimes. Our unification reveals a shared optimization principle: Global Trajectory Score Matching (GTSM), for which gradient boosting (in an idealized version) is asymptotically optimal. We underscore the conceptual value of our work through two key practical instantiations: \treeflow, which achieves competitive generation quality on tabular data with higher fidelity and a 2\times computational speedup, and \dsmtree, a novel distillation method that transfers hierarchical decision logic into neural networks, matching teacher performance within 2\% on many benchmarks.

Paper page on Hugging Face · arXiv

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