Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916759349493760 |
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| author | Liu, Junnan Liu, Hongwei Xiao, Linchen Liu, Shudong Zhang, Taolin Ma, Zihan Zhang, Songyang Chen, Kai |
| author_facet | Liu, Junnan Liu, Hongwei Xiao, Linchen Liu, Shudong Zhang, Taolin Ma, Zihan Zhang, Songyang Chen, Kai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19815 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective Liu, Junnan Liu, Hongwei Xiao, Linchen Liu, Shudong Zhang, Taolin Ma, Zihan Zhang, Songyang Chen, Kai Computation and Language Artificial Intelligence 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. |
| title | Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.19815 |