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

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.

The Discrete Forcing Expert performs discrete action prediction followed by continuous refinement within a partially shared Action DiT conditioned on VLM features. Each branch needs one forward pass.

@article{wang2026discrete,
  title={Discrete Forcing: Infusing Discrete Guidance into Continuous Denoising for Few-Step Action Experts},
  author={Wang, Jingbo and Song, Wenxuan and Yu, Wenhao and Zhao, Han and Wang, Xi and Chen, Jiayi and Wang, Donglin and Wang, Yan and Li, Haoang},
  journal={arXiv preprint arXiv:2609.39526},
  year={2026}
}