Boosting Deductive Reasoning with Step Signals In RLHF

Jialian Li, YipinZhang, Wei Shen, Yuzi Yan, Jian Xie, Dong Yan

Keywords: LLM, RLHF, reasoning

TL;DR: We propose a novel method to generate logical data, and we can improve deductive reasoning ability through RLHF with our data. Our data can also be used to eval the deductive ability of LLMs.

Abstract: Logical reasoning is a crucial task for Large Language Models (LLMs), enabling them to tackle complex problems. Among reasoning tasks, multi-step reasoning poses a particular challenge. Grounded in the theory of formal logic, we have developed an automated method, Multi-step Deduction (MuseD), for deductive reasoning data. MuseD has allowed us to create training and testing datasets for multi-step reasoning. Our generation method enables control over the complexity of the generated instructions, facilitating training and evaluation of models across different difficulty levels. Through RLHF training, our training data has demonstrated significant improvements in logical capabilities for both in-domain of out-of-domain reasoning tasks. Additionally, we have conducted tests to assess the multi-step reasoning abilities of various models.

Primary Area: datasets and benchmarks

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Submission Number: 6281