# Velocity Adversarial Distillation

Few-step diffusion distillation has long relied on estimating the student score, yet at intermediate steps that score is hard to track and the estimated distribution can differ from the one used to update the student. VA-TDM instead supervises velocity fields built from the student's own sampling process, uses a learned critic to compare student and teacher fields and turns their disagreement into generator feedback, unifying score distillation, Fisher matching and moment matching. On four-step SD3.5-Medium it raises GenEval from TDM's 0.61 to 0.67; on 33B MiniMax-H3 at the same 4-NFE budget it wins 61.33% of human comparisons against Fast-H3 and reaches a 45.53% preference rate against the 100-NFE teacher, versus 24.67% for Fast-H3.

Few-step diffusion distillation has long relied on estimating the student score, yet at intermediate steps that score is hard to track and the estimated distribution can differ from the one used to update the student. VA-TDM instead supervises velocity fields built from the student's own sampling process, uses a learned critic to compare student and teacher fields and turns their disagreement into generator feedback, unifying score distillation, Fisher matching and moment matching. On four-step SD3.5-Medium it raises GenEval from TDM's 0.61 to 0.67; on 33B MiniMax-H3 at the same 4-NFE budget it wins 61.33% of human comparisons against Fast-H3 and reaches a 45.53% preference rate against the 100-NFE teacher, versus 24.67% for Fast-H3.