Boosting Deductive Reasoning with Step Signals In RLHF

• URL: http://arxiv.org/abs/2410.09528v2
• Date: Thu, 24 Oct 2024 09:36:53 GMT
• Title: Boosting Deductive Reasoning with Step Signals In RLHF
• Authors: Jialian Li, Yipin Zhang, Wei Shen, Yuzi Yan, Jian Xie, Dong Yan,
• Abstract summary: 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 training data has demonstrated significant improvements in logical capabilities for both in-domain of out-of-domain reasoning tasks.
• Score: 15.441793744822457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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