Summary of N-gram Induction Heads For In-context Rl: Improving Stability and Reducing Data Needs, by Ilya Zisman et al.
N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs
by Ilya Zisman, Alexander Nikulin, Viacheslav Sinii, Denis Tarasov, Nikita Lyubaykin, Andrei Polubarov, Igor Kiselev, Vladislav Kurenkov
First submitted to arxiv on: 4 Nov 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 In this paper, researchers aim to improve the efficiency of reinforcement learning (RL) models by integrating n-gram induction heads into transformers. Existing methods, such as Algorithm Distillation, require large datasets and can be unstable, making them costly to train. The proposed approach reduces data requirements and eases training by incorporating attention patterns that make models less sensitive to hyperparameters. In experiments on grid-world and pixel-based environments, the method matches or surpasses the performance of AD, suggesting improved efficiency for in-context RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists try to make it easier to train AI models that can learn from a few examples without needing updates. They do this by adding special attention patterns to transformers, which helps them need less data and be more stable during training. This new approach performs as well or better than other methods in some environments, making it potentially useful for real-world applications. |
Keywords
» Artificial intelligence » Attention » Distillation » N gram » Reinforcement learning
