Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning

• URL: http://arxiv.org/abs/2407.18248v1
• Date: Thu, 25 Jul 2024 17:59:16 GMT
• Title: Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning
• Authors: Tianduo Wang, Shichen Li, Wei Lu,
• Abstract summary: In this work, we demonstrate that the reasoning abilities of small-scale LMs can be enhanced through self-training. We also show that the conventional self-training can be further augmented by a preference learning algorithm called Direct Preference Optimization.
• Score: 5.487210426671288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective training of language models (LMs) for mathematical reasoning tasks demands high-quality supervised fine-tuning data. Besides obtaining annotations from human experts, a common alternative is sampling from larger and more powerful LMs. However, this knowledge distillation approach can be costly and unstable, particularly when relying on closed-source, proprietary LMs like GPT-4, whose behaviors are often unpredictable. In this work, we demonstrate that the reasoning abilities of small-scale LMs can be enhanced through self-training, a process where models learn from their own outputs. We also show that the conventional self-training can be further augmented by a preference learning algorithm called Direct Preference Optimization (DPO). By integrating DPO into self-training, we leverage preference data to guide LMs towards more accurate and diverse chain-of-thought reasoning. We evaluate our method across various mathematical reasoning tasks using different base models. Our experiments show that this approach not only improves LMs' reasoning performance but also offers a more cost-effective and scalable solution compared to relying on large proprietary LMs.

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