How does fine-tuning improve sensorimotor representations in large language models?

Minghua Wu; Javier Conde; Pedro Reviriego; Marc Brysbaert

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arXiv · 2603.03313

How does fine-tuning improve sensorimotor representations in large language models?

Minghua Wu·Javier Conde·Pedro Reviriego·Marc Brysbaert

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Abstract

Large Language Models (LLMs) exhibit a significant "embodiment gap", where their text-based representations fail to align with human sensorimotor experiences. This study systematically investigates whether and how task-specific fine-tuning can bridge this gap. Utilizing Representational Similarity Analysis (RSA) and dimension-specific correlation metrics, we demonstrate that the internal representations of LLMs can be steered toward more embodied, grounded patterns through fine-tuning. Furthermore, the results show that while sensorimotor improvements generalize robustly across languages and related sensory-motor dimensions, they are highly sensitive to the learning objective, failing to transfer across two disparate task formats.

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Minghua Wu, Javier Conde, Pedro Reviriego, Marc Brysbaert. 2026-02-09. How does fine-tuning improve sensorimotor representations in large language models?. https://arxiv.org/abs/2603.03313

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