Risk-Calibrated Proposal Transport for Finite-Particle Diffusion Steering
Inference-time steering combines pretrained diffusion experts or rewards without retraining by changing the dynamics that transport noise to data. Feynman-Kac correction compensates for proposal mismatch through importance-weighted sequential Monte Carlo (SMC), whose finite-particle behavior depends on the proposal. Variance-controlling guidance (VCG) improves that proposal by fitting a linear drift correction to minimize empirical log-weight-rate variance. Although its population optimum cannot worsen residual variance, finite-particle VCG can nearly eliminate its fitting residual while increasing residual risk on new states by orders of magnitude. The resulting update can degrade unweig...
| Authors | Ziseok Lee, Jaehyeon Kim, Seungwon Kim et al. |
|---|---|
| Primary category | stat.ML |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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