Next-Latent Prediction Transformers [R]
Microsoft Research introduces Next-Latent Prediction, a self-supervised learning method that trains transformers to predict their own next latent state, enabling more efficient reasoning and planning. This approach complements next-token prediction and allows for up to 3.3x faster inference via self-speculative decoding. Builders can explore using NextLat to improve transformer performance and efficiency in their applications. The method has the potential to unlock more compact world models for
Key takeaways
- NextLat trains transformers to predict their own next latent state.
- Enables up to 3.3x faster inference via self-speculative decoding.
- Complements next-token prediction for more efficient reasoning and planning.
Microsoft Research introduces Next-Latent Prediction, a self-supervised learning method that trains transformers to predict their own next latent state, enabling more efficient reasoning and planning. This approach complements next-token prediction and allows for up to 3.3x faster inference via self-speculative decoding. Builders can explore using NextLat to improve transformer performance and efficiency in their applications. The method has the potential to unlock more compact world models for
Key takeaways
- NextLat trains transformers to predict their own next latent state.
- Enables up to 3.3x faster inference via self-speculative decoding.
- Complements next-token prediction for more efficient reasoning and planning.