Efficient high-fidelity 3D content generation remains a challenging task, as state-of-the-art diffusion-based 3D generation models typically require dozens of sampling steps. Though few-step distillation methods such as Consistency Models (CMs) have achieved substantial advancements in accelerating 2D diffusion models, they face challenges when applied to the more complex 3D generation task. In this study, we propose a novel framework for few-step 3D diffusion distillation. Our approach is built upon the primary objective of learning the Marginal Probability Path (MPP), which is the probabilistic transport from the marginal distribution to the data distribution in a single step. To effectively optimize this objective, we propose two complementary loss functions: Velocity Matching (VM), which directly optimizes model parameters through a tractable objective derived from the primary objective, and Velocity Distillation (VD), which matches the student and teacher marginal distributions by leveraging the MPP as a measure. When evaluated on the state-of-the-art 3D generation framework TRELLIS, our method reduces sampling steps for each diffusion transformer from 25 to 1–2, while preserving high visual and geometric fidelity. Extensive experiments demonstrate that our method significantly outperforms existing CM distillation methods, and enables TRELLIS to achieve state-of-the-art performance in few-step 3D generation.