title: Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective
summary: 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.
paper_authors._id: 683523b21a2911c0774a1dc5
paper_authors.name: Junnan Liu
paper_authors.status: claimed_verified
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paper_authors.fullname: Junnan Liu
paper_authors.type: user
paper_authors.user: jnanliu
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paper_authors.name: Hongwei Liu
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paper_authors.name: Linchen Xiao
paper_authors._id: 683523b21a2911c0774a1dc8
paper_authors.name: Shudong Liu
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paper_authors.fullname: Shudong Liu
paper_authors.type: user
paper_authors.user: Sudanl
paper_authors._id: 683523b21a2911c0774a1dc9
paper_authors.name: Taolin Zhang
paper_authors.status: admin_assigned
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paper_authors.fullname: Taolin Zhang
paper_authors.type: user
paper_authors.user: ruxian
paper_authors._id: 683523b21a2911c0774a1dca
paper_authors.name: Zihan Ma
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paper_authors.fullname: Zihan Ma
paper_authors.type: user
paper_authors.user: MichaelErchi
paper_authors._id: 683523b21a2911c0774a1dcb
paper_authors.name: Songyang Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:51:54.358Z
paper_authors._id: 630716d11801ecc7d2595021
paper_authors.avatarUrl: /avatars/2d36a880ce4a3cf7efc5ff3987dbeaf3.svg
paper_authors.fullname: Songyang Zhang
paper_authors.type: user
paper_authors.user: zsytony
paper_authors._id: 683523b21a2911c0774a1dcc
paper_authors.name: Kai Chen
paper_title: Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective
paper_summary: 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.
paper_ai_summary: LLM reasoning is understood through a meta-learning framework, treating reasoning as pseudo-gradient descent and questions as individual tasks, which enhances generalization and provides practical insights for improvement.
paper_ai_keywords: large language models meta-learning pseudo-gradient descent inner loop optimization generalization fundamental reasoning capabilities