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Learn about RATIONALYST, a novel model designed to improve process supervision of reasoning in large language models by mining implicit rationales from web-scale data. Discover how this approach addresses the challenge of incomplete reasoning steps that LLMs generate by mimicking logical leaps common in everyday communication. Explore the methodology for extracting 79,000 rationales from unlabeled datasets including the Pile and various reasoning datasets with minimal human intervention. Understand how web-scale pre-training enables RATIONALYST to generalize across diverse reasoning tasks spanning mathematical, commonsense, scientific, and logical domains. Examine the performance improvements achieved by fine-tuning LLaMa-3-8B, resulting in an average 3.9% accuracy increase across seven representative reasoning benchmarks. Compare RATIONALYST's superior performance against significantly larger verifiers like GPT-4 and similarly sized models trained on equivalent datasets, demonstrating the effectiveness of this process supervision approach for enhancing reasoning capabilities in language models.
