reviews: 
Comments: The paper proposes denoising generative model called shortcut model. These models are trained end-to-end and can generate samples in both single-step and few-step setting. The core idea is to condition the model on step size in addition to time step, which is typically done in conventional diffusion models. The hypothesis is that the additional conditioning on step size allows shortcut models to account for the future curvature. The model is trained via a loss objective that combines the optimal-transport flow matching loss with a self-consistency loss. The self-consistency loss is enforced by ensuring that the prediction of model at a given skip size matches the target obtained by two sequential smaller skips (for the same skip distance) with the shortcut model. The high computational cost as well as variance during training is dealt with by computing self-consistency loss on a small subset of samples in the batch. The proposed method has been tested on CelebAHQ-256 and ImageNet-256, and has good single-step image generation quality.
Strengths: 1. _Simplicity_: The proposed method is simple, easy to understand and simplifies some challenges previously encountered in other methods to obtain one-step or few-step models. For eg. It gets rid of two-stage training required for diffusion distillation as well as complex training scheduling required for consistency models. The loss objective is intuitive and builds upon flow matching loss and introduces an additional self-consistency condition. 
2. _Writing_: The paper is well-written. The core method has been explained well. The paper talks about challenges that can be encountered while training shortcut models and ways to address these challenges. 
3. _Computational efficiency_: The method seems computationally efficient as with an additional (marginal) cost of training diffusion models, shortcut models can be trained to generate samples in single-step as well as few steps. Further, even on larger datasets such as ImageNet-256 and CelebAHQ-256, these models can be trained in 1-2 days (though the exact number of TPUv3 nodes is unclear).  
4. _Applications_: The authors demonstrate advantages of this method on both image-generation as well as on robotic control tasks showing ways to generate an iterative sequence of actions in a single denoising step per action.
Weaknesses: Note: The template of this paper seems to be different and doesn’t have line numbers present in the standard template for papers under review. 

1. The proposed method for shortcut sampling is general and should work for any gaussian probability paths ($x_t = \alpha_t x_0 + \sigma_t \epsilon$) however this hasn’t been explored or described in the paper. The paper considers only one choice of optimal transport probability path ($x_t = (1-t) x_0 + t \epsilon$). However, nothing constraints the method to be restricted to this particular choice. (One motivation for this choice of parametrization could be that OT paths are said to have less curvature but in this case, these are conditional OT paths and are not exactly straight lines paths between $x_1$ and $x_0$.) It is worth exploring ablations on how shortcut models perform on other parametrization of gaussian probability paths/sampling paths. In addition, the text should also be updated to have a more general parametrization.
2. The paper should also include quantitative performance on more commonly used datasets such as ImageNet-64 and CIFAR-10. While reporting results on larger datasets such as CelebAHQ-256 and ImageNet-256 is impressive, reporting metrics on these smaller datasets is also important. Most of the prior papers report metrics on these datasets which makes it easier to compare the performance of shortcut models to a larger number of previously proposed methods/models which are not currently reported in the paper (Note: I understand that it is impossible to replicate all the previously proposed methods due to the vastness of literature in the space).
3. Minor: I personally feel that images such as in Figure 1 are misleading as the original flow-matching objective was not specifically meant for single step generation, and the performance is expected to deteriorate because these models don’t learn an ODE integrator. There are some advantages of the original flow-matching models as well over shortcut models. These models are continuous time models which can be evaluated at any time between [0, 1] whereas shortcut models are discrete time and can only be evaluated at specific discrete time steps.
Recommendation: 8
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3