Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Article Swipe
Junnan Liu
,
Hongwei Liu
,
Linchen Xiao
,
Shudong Liu
,
Taolin Zhang
,
Zihan Ma
,
Songyang Zhang
,
Kai Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2505.19815
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2505.19815
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.
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- http://arxiv.org/abs/2505.19815
- https://arxiv.org/pdf/2505.19815
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https://openalex.org/W4414587198Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2505.19815Digital Object Identifier
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Deciphering Trajectory-Aided LLM Reasoning: An Optimization PerspectiveWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-05-26Full publication date if available
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Junnan Liu, Hongwei Liu, Linchen Xiao, Shudong Liu, Taolin Zhang, Zihan Ma, Songyang Zhang, Kai ChenList of authors in order
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https://arxiv.org/abs/2505.19815Publisher landing page
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https://arxiv.org/pdf/2505.19815Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2505.19815Direct OA link when available
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0Total citation count in OpenAlex
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