过多思维链示例反而降低模型推理能力，新方法可提升300%表现
To Think or Not to Think: The Hidden Cost of Meta-Training with Excessive CoT Examples
- 通过调节元训练中思维链与非思维链样本比例来优化学习
- 在无思维链示例时仍使准确率提升最高达300%
- 适用于需要抽象推理的大型语言模型，尤其适合符号推理任务
思维链提示结合少量示例上下文学习（ICL）显著提升了大语言模型的推理能力。然而，当预训练知识不足时，带有思维链示例的ICL在新任务上效果不佳。我们使用CoT-ICL Lab框架在受控环境下研究该问题，并提出元训练技术以实现上下文中的抽象推理任务学习。尽管思维链示例有助于推理，但我们发现其在元训练中过度引入会因思维链监督不足而损害性能。为此，我们提出CoT-Recipe，一种形式化方法，用于调节元训练序列中思维链与非思维链示例的混合比例。实验表明，通过CoT-Recipe精心调节，即使上下文中无思维链示例，Transformer模型在新任务上的准确率也能提升最高达300%。我们将该方法应用于Qwen2.5系列预训练LLM进行符号推理任务，准确率最高提升130%。
原文摘要 · Abstract (English)
Chain-of-thought (CoT) prompting combined with few-shot in-context learning (ICL) has unlocked significant reasoning capabilities in large language models (LLMs). However, ICL with CoT examples is ineffective on novel tasks when the pre-training knowledge is insufficient. We study this problem in a controlled setting using the CoT-ICL Lab framework, and propose meta-training techniques to learn novel abstract reasoning tasks in-context. Although CoT examples facilitate reasoning, we noticed that their excessive inclusion during meta-training degrades performance when CoT supervision is limited. To mitigate such behavior, we propose CoT-Recipe, a formal approach to modulate the mix of CoT and non-CoT examples in meta-training sequences. We demonstrate that careful modulation via CoT-Recipe can increase the accuracy of transformers on novel tasks by up to 300% even when there are no CoT examples available in-context. We confirm the broader effectiveness of these techniques by applying them to pretrained LLMs (Qwen2.5 series) for symbolic reasoning tasks and observing gains of up to 130% in accuracy.
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