RATIONALYST: Pre-training Process-Supervision for Improving Reasoning
<|reference_start|>RATIONALYST: Pre-training Process-Supervision for Improving Reasoning: The reasoning steps generated by LLMs might be incomplete, as they mimic logical leaps common in everyday communication found in their pre-training data: underlying rationales are frequently left implicit (unstated). To address this challenge, we introduce RATIONALYST, a model for process-supervision of reasoning based on pre-training on a vast collection of rationale annotations extracted from unlabeled data. We extract 79k rationales from web-scale unlabelled dataset (the Pile) and a combination of reasoning datasets with minimal human intervention. This web-scale pre-training for reasoning allows RATIONALYST to consistently generalize across diverse reasoning tasks, including mathematical, commonsense, scientific, and logical reasoning. Fine-tuned from LLaMa-3-8B, RATIONALYST improves the accuracy of reasoning by an average of 3.9% on 7 representative reasoning benchmarks. It also demonstrates superior performance compared to significantly larger verifiers like GPT-4 and similarly sized models fine-tuned on matching training sets.<|reference_end|>
@article{jiang2024rationalyst:,
    title={RATIONALYST: Pre-training Process-Supervision for Improving Reasoning},
    author={Dongwei Jiang, Guoxuan Wang, Yining Lu, Andrew Wang, Jingyu Zhang,
  Chuyu Liu, Benjamin Van Durme, Daniel Khashabi},
    journal={arXiv preprint arXiv:2410.01044},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01044},
    primaryClass={cs.AI cs.CL}
}