Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective
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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Junnan Liu
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2025-05-27T07:51:56.495Z
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Junnan Liu
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Hongwei Liu
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Linchen Xiao
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Shudong Liu
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2025-05-27T08:15:12.824Z
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Shudong Liu
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Sudanl
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Taolin Zhang
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2025-05-27T12:31:07.877Z
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Taolin Zhang
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ruxian
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Zihan Ma
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2025-06-03T09:39:25.103Z
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Zihan Ma
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MichaelErchi
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Songyang Zhang
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2025-05-27T07:51:54.358Z
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Songyang Zhang
user
zsytony
683523b21a2911c0774a1dcc
Kai Chen
Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective
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.
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.
large language models meta-learning pseudo-gradient descent inner loop optimization generalization fundamental reasoning capabilities