Author

Akifumi Wachi

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#machine learning Preprint Sep 2026

Steepest Guidance: A Practical and Principled Approach to Inference-Time Alignment of Flow and Diffusion-based Models

Inference-time alignment of flow and diffusion-based models is critical for achieving flexible generative modeling. Theoretically, Doob's $h$-transform provides an elegant solution to this problem, and most existing methods are based on this principle. However, in practice, estimating the optimal guidance derived from...

Shokichi Takakura, Akifumi Wachi, Rei Higuchi et al. · 0 citations

#small language model Preprint Aug 2026

Safety Hacking in Constrained Best-of-$N$ Inference-time Scaling

It is shown that policies within a bounded $\chi^2$ divergence from the proxy-feasible reference distribution admit an $N$-independent safety-hacking bound, and instantiate this general coverage-control principle with constrained pessimistic sampling.

Akifumi Wachi, Takumi Tanabe, Youhei Akimoto · 0 citations