Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective

TL;DR

This paper introduces a meta-learning framework to understand and enhance large language model reasoning by viewing reasoning trajectories as pseudo-gradient updates, demonstrating strong generalization and practical insights.

Contribution

It formalizes LLM reasoning as a meta-learning process, revealing parallels with gradient-based methods and offering new avenues for model improvement.

Findings

Reasoning trajectories resemble pseudo-gradient descent updates.

Meta-learning perspective explains LLM generalization to unseen questions.

Empirical results confirm the connection between LLM reasoning and meta-learning.

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.