Title: pi-Flow: Policy-Based Few-Step Generation via Imitation Distillation
Primary area: generative models
Keywords: diffusion models, flow models, few-step generation, distillation

Abstract: Few-step diffusion or flow-based generative models typically distill a velocity-predicting teacher into a student that predicts a shortcut towards denoised data. This format mismatch has led to complex distillation procedures that often suffer from a quality--diversity trade-off. To address this, we propose policy-based flow models ($\pi$-Flow). 
$\pi$-Flow modifies the output layer of a student flow model to predict a network-free policy at one timestep. The policy then produces dynamic flow velocities at future substeps with negligible overhead, enabling fast and accurate ODE integration without extra network evaluations.
To match the policy's ODE trajectory to the teacher's,
we introduce a novel imitation distillation approach, which matches the policy's velocity to the teacher’s along the policy's trajectory using a standard $\ell_2$ flow matching loss. 
By simply mimicking the teacher's behavior, $\pi$-Flow enables stable and scalable training and avoids the quality--diversity trade-off.
On ImageNet $256\times 256$, it attains a 1-NFE FID of 2.9, outperforming previous 1-NFE models of the same architecture. 
On FLUX.1-12B and Qwen-Image-20B at 4 NFEs, $\pi$-Flow achives substantially better diversity than state-of-the-art few-step methods, while maintaining teacher-level quality.
Code and models will be released publicly.