Learning Inference Clocks for Finite-Step Generative ODEs

Finite-step sampling from generative ODEs depends on where a solver evaluates the frozen vector field. We present Generative Inference Clock Optimization (GICO), an offline policy-optimization method for utility-specific allocation with the generator and solver rule fixed. Each clock is a normalized flow-time density whose inverse CDF yields a grid at the requested evaluation budget. A surrogate fitted to completed-solve utilities weights reference densities to anchor a policy conditioned on context, solver, and budget, and guides regularized refinement without further generator evaluations or gradients. An exact finite-budget adjoint identity explains utility sensitivity; smooth asymptotics yield an allocation surrogate. Experiments cover temporal sequences, molecular 3D coordinates, and images. Temporal errors decrease by up to 10.0% and Kabsch RMSD by 5.4–7.9% relative to uniform grids. Deterministic GICO attains the lowest mean FID among evaluated methods in 10/12 low-resolution image settings; its stochastic extension leads in the remaining two. FID reductions relative to fixed-reference envelopes reach 30.9%. With the full observation budget on frozen SANA, GICO policies outperform observation-matched Bayesian optimization and PPO-style clock policies on ImageReward and VQAScore at all tested NFEs. Held-out evaluations and ablations further support generalization across contexts and inference budgets without refitting.