Title: Scaling Code-Assisted Chain-of-Thoughts and Instructions for Model Reasoning Show Chinese title

Title: 缩放代码辅助思维链和模型推理的指令

Abstract: Reasoning capability is pivotal for Large Language Models (LLMs) to solve complex tasks, yet achieving reliable and scalable reasoning remains challenging. While Chain-of-Thought (CoT) prompting has become a mainstream approach, existing methods often suffer from uncontrolled generation, insufficient quality, and limited diversity in reasoning paths. Recent efforts leverage code to enhance CoT by grounding reasoning in executable steps, but such methods are typically constrained to predefined mathematical problems, hindering scalability and generalizability. In this work, we propose Caco (Code-Assisted Chain-of-ThOught), a novel framework that automates the synthesis of high-quality, verifiable, and diverse instruction-CoT reasoning data through code-driven augmentation. Unlike prior work, Caco first fine-tunes a code-based CoT generator on existing math and programming solutions in a unified code format, then scales the data generation to a large amount of diverse reasoning traces. Crucially, we introduce automated validation via code execution and rule-based filtering to ensure logical correctness and structural diversity, followed by reverse-engineering filtered outputs into natural language instructions and language CoTs to enrich task adaptability. This closed-loop process enables fully automated, scalable synthesis of reasoning data with guaranteed executability. Experiments on our created Caco-1.3M dataset demonstrate that Caco-trained models achieve strong competitive performance on mathematical reasoning benchmarks, outperforming existing strong baselines. Further analysis reveals that Caco's code-anchored verification and instruction diversity contribute to superior generalization across unseen tasks. Our work establishes a paradigm for building self-sustaining, trustworthy reasoning systems without human intervention. Show Chinese abstract

Abstract: 推理能力对于大型语言模型（LLMs）解决复杂任务至关重要，但实现可靠且可扩展的推理仍然具有挑战性。 尽管思维链（CoT）提示已成为主流方法，但现有方法通常存在生成不可控、质量不足以及推理路径多样性有限的问题。 最近的努力通过将推理建立在可执行步骤上，利用代码来增强CoT，但此类方法通常局限于预定义的数学问题，阻碍了可扩展性和泛化能力。 在本工作中，我们提出了Caco（Code-Assisted Chain-of-ThOught），一种新的框架，通过代码驱动的增强自动合成高质量、可验证和多样化的指令-CoT推理数据。 与之前的工作不同，Caco首先在一个统一的代码格式下对基于代码的CoT生成器进行微调，然后将数据生成扩展到大量多样化的推理轨迹。 关键的是，我们引入了通过代码执行和基于规则的过滤进行的自动化验证，以确保逻辑正确性和结构多样性，随后将过滤后的输出逆向工程为自然语言指令和语言CoTs，以丰富任务适应性。 这种闭环过程实现了保证可执行性的推理数据的完全自动化、可扩展的合成。 我们在创建的Caco-1.3M数据集上的实验表明，Caco训练的模型在数学推理基准测试中表现出强大的竞争力，优于现有的强基线。 进一步分析表明，Caco的代码锚定验证和指令多样性有助于在未见过的任务上实现优越的泛化能力。 我们的工作建立了一个无需人工干预的构建自我维持、可信推理系统的范式。