Discrete Forcing: Infusing Discrete Guidance into Continuous Denoising for Few-Step Action Experts

Abstract:Efficient action generation in vision-language-action (VLA) models requires capturing both coarse action structure and fine-grained details. Discrete action tokens provide compact structural representations but sacrifice precision, while continuous action tokens offer high precision but often require multiple denoising steps. We introduce Discrete Forcing, a flow-matching framework that combines these representations through an explicit coarse-to-fine generation process. It first predicts discrete action tokens to establish a coarse action structure, then uses them to guide continuous action refinement. The discrete and continuous components share a common diffusion transformer backbone with specialized branches, maintaining a parameter count comparable to a conventional single-branch model while requiring only one forward pass per branch. Extensive evaluations across multiple benchmarks demonstrate improved performance and faster inference over a parameter-matched continuous action expert, with consistent performance gains as model capacity increases. Real-world experiments further demonstrate improvements on high-precision and dynamic manipulation tasks.

Robotics (cs.RO)

arXiv:2609.39526 [cs.RO]

(or arXiv:2609.39526v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.39526 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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