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On the Empirical Complexity of Reasoning and Planning in LLMs
We found that if problems can be decomposed into a sequence of reasoning steps and learning to predict the next step has a low sample and computational complexity, explicitly outlining the reasoning chain with all necessary information for predicting the next step may improve performance.
Continual Learning of Numerous Tasks from Long-tail Distributions
In this paper, we investigate the performance of continual learning algorithms with a large number of tasks drawn from a task distribution that is long-tail in terms of task sizes.
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