Author

Na Li

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#machine learning Preprint Sep 2026

D-IMPL: A Diffusion-based Solver for Parameterized BBOs

This paper proposes Diffusion-based Iterative Minimization Policy Learning (D-IMPL), a practical generative-model-based solver for solving parameterized BBOs that employs diffusion models to learn a minimization policy, whose density is proportional to the exponential of the negated objective values, thereby amortizing...

Yang Hu, Na Li · 0 citations

Preprint Aug 2026

ConvergeFlow: Language Flow with Provable Convergence to Token Embeddings

An embedding-space flow-based LM, which constrains the data predictor to the convex hull of token embeddings and trains it solely with the mean squared error objective induced by flow matching, enabling direct token prediction without a CE-supervised decoder.

Na Li, Yuchen Jiao, Chang-Xiao Cai et al. · 2 citations