Summary of Diffusion Forcing: Next-token Prediction Meets Full-sequence Diffusion, by Boyuan Chen et al.
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
by Boyuan Chen, Diego Marti Monso, Yilun Du, Max Simchowitz, Russ Tedrake, Vincent Sitzmann
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Diffusion Forcing, a novel training paradigm for sequence generative models. The approach combines the strengths of next-token prediction models and full-sequence diffusion models to generate variable-length sequences with desirable trajectories. By training a causal next-token prediction model to denoise tokens with independent noise levels, our method achieves marked performance gains in decision-making and planning tasks. Additionally, Diffusion Forcing optimizes a variational lower bound on the likelihoods of all subsequences of tokens drawn from the true joint distribution. This paper presents a new method for sequence generative modeling that can roll out sequences of continuous tokens, such as video, with lengths past the training horizon. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to train machines to generate text or other sequences. The approach is based on combining two different methods to get the best results. It works by adding noise to individual parts of the sequence and then training the machine to correct that noise. This leads to better performance in tasks like decision-making and planning. The method also allows for longer sequences, such as video, to be generated. Overall, this new approach has potential applications in areas like artificial intelligence. |
Keywords
* Artificial intelligence * Diffusion * Token
