The authors formalized inference-time alignment as an optimization problem over probability measures, where the objective is a reward functional (linear or non-linear).
Steepest Guidance aligns generative models by maximizing local reward improvement
The method tackles both linear and non-linear reward functionals and provides provable convergence guarantees.
Top University
Shokichi Takakura · Akifumi Wachi · Rei Higuchi · Kohei Miyaguchi · Taiji Suzuki
LY Corporation · The University of Tokyo · RIKEN AIP
Research Digest··2 min read
Takakura et al.
Why this paper
From RIKEN AIP and 2 others
In one line
Inference-time alignment of flow and diffusion models can be cast as sequential optimization in probability-measure space, where steepest local ascent of the reward functional gives unbiased guidance.
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