Faster Density Estimation and Inference-Time Steering for Flow Language Models

Flow language models generate discrete data by numerical integrations in a continuous space using a learned denoiser. In order to estimate log-densities, one typically needs to estimate the divergence of the learned network. This is often achieved using the Hutchinson's estimator, which incurs notable computational overhead. We propose an alternative density estimation mechanism that is consistent, deterministic, has negligible computational overhead over denoiser evaluations and applicable to real models, and unlocks applications of ODE simulations for general inference-time steering with validations on small and analytical settings.