大模型生成能力比判断力更关键，决定思维树能否成功
Understanding When Tree of Thoughts Succeeds: Larger Models Excel in Generation, Not Discrimination
- 分离分析生成与判别模块，发现生成环节起主导作用
- 扩大生成器可显著提升表现，判别器升级效果有限
- 适合研究大模型推理机制或优化思维树策略的读者
思维树（ToT）是一种针对大语言模型的推理策略，通过生成器提出推理步骤，判别器决定采纳哪些步骤。ToT在推理任务中表现优异，常优于输入输出提示和思维链方法。然而，其并非在所有模型上都持续领先，关于其何时最有效仍存在知识空白。本文分别分析生成器与判别器的作用，发现生成器比判别器更为关键。扩大生成器能显著提升ToT性能，即使使用小模型作为判别器；而固定生成器仅扩大判别器，收益微弱。结果表明，不同规模模型在判别能力上接近，但在生成能力上差异显著。
原文摘要 · Abstract (English)
Tree of Thoughts (ToT) is a reasoning strategy for Large Language Models (LLMs) that employs a generator to suggest reasoning steps and a discriminator to decide which steps to implement. ToT demonstrates strong performance on reasoning tasks, often surpassing simple methods such as Input-Output (IO) prompting and Chain-of-Thought (CoT) reasoning. However, ToT does not consistently outperform such simpler methods across all models, leaving large knowledge gaps on the conditions under which ToT is most beneficial. In this paper, we analyze the roles of the generator and discriminator separately to better understand the conditions when ToT is beneficial. We find that the generator plays a more critical role than the discriminator in driving the success of ToT. Scaling the generator leads to notable improvements in ToT performance, even when using a smaller model as the discriminator, whereas scaling the discriminator with a fixed generator yields only marginal gains. Our results show that models across different scales exhibit comparable discrimination capabilities, yet differ significantly in their generative performance for ToT.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。