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.
View on arXiv@article{liu2025_2505.19815,
title={ Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective },
author={ Junnan Liu and Hongwei Liu and Linchen Xiao and Shudong Liu and Taolin Zhang and Zihan Ma and Songyang Zhang and Kai Chen },
journal={arXiv preprint arXiv:2505.19815},
year={ 2025 }
}