Since the sampling probability is 1/K independent of the input, you don't need to compute K different intermediate outputs at each layer during inference, you can instead decide ahead of time which of the outputs you want to use and only compute that one.

(This is mentioned in Q1 in the "Common Questions About DDN" section at the bottom.)

Thank you for your appreciation. I will update the future work on both GitHub and Twitter.

https://github.com/DIYer22 https://x.com/diyerxx

I believe DDN is exceptionally well-suited to the “generative models for discriminative tasks” paradigm for object detection.

Much like DiffusionDet, which applies diffusion models to detection, DDN can adopt the same philosophy. I expect DDN to offer several advantages over diffusion-based approaches: - Single forward pass to obtain results, no iterative denoising required. - If multiple samples are needed (e.g., for uncertainty estimation), DDN can directly produce multiple outputs in one forward pass. - Easy to impose constraints during generation due to DDN's Zero-Shot Conditional Generation capability. - DDN supports more efficient end-to-end optimization, thus more suitable for integration with discriminative models and reinforcement learning.

I believe it is the novelty. Here I would like to quote Reviewer r4YK’s original words:

> Many high rated papers would have been done by someone else if their authors never published them or were rejected. However, if this paper is not published, it is not likely that anyone would come up with this approach. This is real publication value. I am reminding again the original diffusion paper from 2015 (Sohl-Dickstein) that was almost not noticed for 5 years. Had it not been published, would we have had the amazing generative models we have today?

Cite from: https://openreview.net/forum?id=xNsIfzlefG&noteId=Dl4bXmujh1

Besides, we compared DDN with other approaches in the Table 1 of original paper, including VQ-VAE.

This is a great figure

Yes, it's absolutely possible—just like how diffusion LLMs work, we can do the same with DDN LLMs.

I made an initial attempt to combine [DDN with GPT](https://github.com/Discrete-Distribution-Networks/Discrete-D...), aiming to remove tokenizers and let LLMs directly model binary strings. In each forward pass, the model adaptively adjusts the byte length of generated content based on generation difficulty (naturally supporting speculative sampling).

The posts mentions that: https://github.com/Discrete-Distribution-Networks/Discrete-D...

The first version of DDN was developed in less than three months, almost entirely by one person. Consequently, the experiments were preliminary and the results far from SoTA.

The current goal in research is scaling up. Here are some thoughts in blog about future directions: https://github.com/Discrete-Distribution-Networks/Discrete-D...

ICLR 2026 reviews are happening now (or soon). This paper here was accepted at ICLR 2025.

I've been thinking about this too—how different DDN is from other generative models. The idea of generating multiple outputs at once in a single pass sounds like it could really speed things up, especially for tasks where you need a bunch of samples quickly. I'm curious how this compares to something like GANs, which can also generate multiple samples but often struggle with mode collapse.

The zero-shot conditional generation part is wild. Most methods rely on gradients or fine-tuning, so I wonder what makes DDN tick there. Maybe the tree structure of the latent space helps navigate to specific conditions without needing retraining? Also, I'm intrigued by the 1D discrete representation—how does that even work in practice? Does it make the model more interpretable?

The Split-and-Prune optimizer sounds new—I'd love to see how it performs against Adam or SGD on similar tasks. And the fact that it's fully differentiable end-to-end is a big plus for training stability.

I also wonder about scalability—can this handle high-res images without blowing up computationally? The hierarchical approach seems promising, but I'm not sure how it holds up when moving from simple distributions to something complex like natural images.

Overall though, this feels like one of those papers that could really shift the direction of generative models. Excited to dig into the code and see what kind of results people get with it!

No, DDN and VQ-VAE are clearly different.

Similarities: - Both map data to a discrete latent space.

Differences: - VQ-VAE needs an external prior over code indices (e.g. PixelCNN or a hierarchical prior) to model distribution. DDN builds its own hierarchical discrete distribution and can even act as the prior for a VQ-VAE-like system. - DDN’s K outputs are features that change with the input; VQ-VAE’s codebook is a set of independent parameters (embeddings) that remain fixed regardless of the input. - VQ-VAE produces a 2-D grid of code indices; DDN yields a 1-D/tree-structured latent. - VQ-VAE needs Straight-Through Estimator. - DDN supports zero-shot conditional generation.

So I’d call them complementary rather than “80 % the same.” (See the paper’s “Connections to VQ-VAE.”)