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Chain of thought prompting successfully improves the reasoning capabilitiesof large language models, achieving state of the art results on a range ofdatasets. However, these reasoning capabilities only appear to emerge in modelswith a size of over 100 billion parameters. In this paper, we explore thetransfer of such reasoning capabilities to models with less than 100 billionparameters via knowledge distillation. Specifically, we finetune a studentmodel on the chain of thought outputs generated by a larger teacher model. Ourexperiments show that the proposed method improves task performance acrossarithmetic, commonsense and symbolic reasoning datasets. For example, theaccuracy of T5 XXL on GSM8K improves from 8.11% to 21.99% when finetuned onPaLM-540B generated chains of thought.

Teaching Small Language Models to Reason