Summary of Transformers Can Navigate Mazes with Multi-step Prediction, by Niklas Nolte et al.
Transformers Can Navigate Mazes With Multi-Step Prediction
by Niklas Nolte, Ouail Kitouni, Adina Williams, Mike Rabbat, Mark Ibrahim
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper studies the limitation of transformers in language modeling, specifically their struggle with long-term planning. The authors argue that standard next token prediction objectives do not provide an explicit mechanism to predict multiple steps ahead or revisit previous paths. To address this, they introduce MLM-U, a new objective that explicitly predicts multiple steps ahead and backwards. They train parameter-matched transformers using both the standard next token prediction and MLM-U objectives, and find that MLM-U significantly improves maze navigation abilities across different types and complexities of mazes. Additionally, MLM-U training is more sample-efficient and converges faster than standard next token training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are really good at understanding language, but they struggle to plan ahead. Imagine trying to navigate a maze without being able to see what’s around the corner! The authors of this paper wanted to fix this problem by creating a new way for transformers to predict multiple steps ahead and revisit previous paths. They tested their approach on mazes and found that it worked much better than the old way. This is exciting because it could help us use transformers for more complex tasks like planning and decision-making. |
Keywords
* Artificial intelligence * Token
