We develop Newton Matching, a unified framework for fine-tuning and sampling in generative modeling.
The target is $π\proptoμe^{τr}$, where $r$ is the reward, $τ>0$ the inverse temperature, and $μ$ denotes the pretrained model's terminal density for fine-tuning or the constant $1$ for sampling.
We shift the paradigm from isolated losses to iterative optimization over canonical models: population minimizers of standard conditional matching for terminal densities.
Under compatible smooth-realization assumptions, canonical velocities form a manifold diffeomorphic to the density manifold.
Transporting the Fisher-Rao metric and mixture connection to this manifold, we show that the reverse-KL Hessian equals the metric, so the Newton direction coincides with the negative Fisher-Rao gradient.
At terminal density $ρ$, each stage takes a tangential step generated by the regularized reward $r-\frac1τ\log(ρ/μ)$, followed by terminal-density-preserving canonicalization.
This canonical retraction yields an exact finite-stepsize density characterization.
For the ideal iteration, we prove strict reverse-KL descent away from the target for $0 < η\le τ$, global convergence under mild conditions, and local quadratic convergence for full steps ($η=τ$).
Covariance and gradient forms, each with forward or reverse regression-pair constructions, yield sample-wise tangential-update losses with the same population minimizer, without importance sampling or full-trajectory backpropagation.
We develop approximate updates and define critical-point consistency as vanishing tangential displacement if and only if $ρ=π$.
We recover representative methods as exact realizations, critical-point-consistent approximations, or objective-altering variants, enabling modular algorithm design.
Our work advances the theory and algorithms of reinforcement learning for generative models.