N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs

• URL: http://arxiv.org/abs/2411.01958v1
• Date: Mon, 04 Nov 2024 10:31:03 GMT
• Title: N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs
• Authors: Ilya Zisman, Alexander Nikulin, Andrei Polubarov, Nikita Lyubaykin, Vladislav Kurenkov,
• Abstract summary: In-context learning allows models like transformers to adapt to new tasks without updating their weights. Existing in-context RL methods, such as Algorithm Distillation (AD), demand large, carefully curated datasets. In this work we integrated the n-gram induction heads into transformers for in-context RL.
• Score: 42.446740732573296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-context learning allows models like transformers to adapt to new tasks from a few examples without updating their weights, a desirable trait for reinforcement learning (RL). However, existing in-context RL methods, such as Algorithm Distillation (AD), demand large, carefully curated datasets and can be unstable and costly to train due to the transient nature of in-context learning abilities. In this work we integrated the n-gram induction heads into transformers for in-context RL. By incorporating these n-gram attention patterns, we significantly reduced the data required for generalization - up to 27 times fewer transitions in the Key-to-Door environment - and eased the training process by making models less sensitive to hyperparameters. Our approach not only matches but often surpasses the performance of AD, demonstrating the potential of n-gram induction heads to enhance the efficiency of in-context RL.

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