Abstract
We propose a novel framework for comprehending the reasoning capabilities of large language models (LLMs) through the perspective of meta-learning. By conceptualizing reasoning trajectories as pseudo-gradient descent updates to the LLM's parameters, we identify parallels between LLM reasoning and various meta-learning paradigms. We formalize the training process for reasoning tasks as a meta-learning setup, with each question treated as an individual task, and reasoning trajectories serving as the inner loop optimization for adapting model parameters. Once trained on a diverse set of questions, the LLM develops fundamental reasoning capabilities that can generalize to previously unseen questions. Extensive empirical evaluations substantiate the strong connection between LLM reasoning and meta-learning, exploring several issues of significant interest from a meta-learning standpoint. Our work not only enhances the understanding of LLM reasoning but also provides practical insights for improving these models through established meta-learning techniques.
Abstract (translated)
我们提出了一种新颖的框架，通过元学习的角度来理解大规模语言模型（LLMs）的推理能力。我们将推理轨迹视为对LLM参数进行伪梯度下降更新，并在此过程中发现了LLM推理与各种元学习范式之间的相似性。我们将用于推理任务的训练过程形式化为一个元学习设置，在这种设置中，每个问题都被视为独立的任务，而推理轨迹则作为内循环优化以适应模型参数。经过一系列多样化的提问训练后，LLM能够发展出可以推广到未见过的问题的基本推理能力。 大量的实证评估证实了LLM推理与元学习之间存在强有力的联系，并探讨了一些从元学习角度来看具有重要意义的问题。我们的工作不仅增强了对LLM推理的理解，还为通过已建立的元学习技术改进这些模型提供了实用见解。
URL
https://arxiv.org/abs/2505.19815
