Efficient Single-Pass Training for Multi-Turn Reasoning

• URL: http://arxiv.org/abs/2504.18246v1
• Date: Fri, 25 Apr 2025 10:46:56 GMT
• Title: Efficient Single-Pass Training for Multi-Turn Reasoning
• Authors: Ritesh Goru, Shanay Mehta, Prateek Jain,
• Abstract summary: Fine-tuning Large Language Models on multi-turn reasoning datasets presents a unique challenge.<n>This paper proposes a novel approach that overcomes this limitation through response token duplication and a custom attention mask.<n>Our approach significantly reduces the training time and allows efficient fine-tuning on multi-turn reasoning datasets.
• Score: 13.831457888508892
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training Large Language Models ( LLMs) to generate explicit reasoning before they produce an answer has been shown to improve their performance across various tasks such as mathematics and coding. However, fine-tuning LLMs on multi-turn reasoning datasets presents a unique challenge: LLMs must generate reasoning tokens that are excluded from subsequent inputs to the LLM. This discrepancy prevents us from processing an entire conversation in a single forward pass-an optimization readily available when we fine-tune on a multi-turn non-reasoning dataset. This paper proposes a novel approach that overcomes this limitation through response token duplication and a custom attention mask that enforces appropriate visibility constraints. Our approach significantly reduces the training time and allows efficient fine-tuning on multi-turn reasoning datasets.

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