Meta-Learning Neural Mechanisms rather than Bayesian Priors

• URL: http://arxiv.org/abs/2503.16048v1
• Date: Thu, 20 Mar 2025 11:33:59 GMT
• Title: Meta-Learning Neural Mechanisms rather than Bayesian Priors
• Authors: Michael Goodale, Salvador Mascarenhas, Yair Lakretz,
• Abstract summary: We investigate the meta-learning of formal languages and find that, contrary to previous claims, meta-trained models are not learning simplicity-based priors.<n>We find evidence that meta-training imprints neural mechanisms into the model, which function like cognitive primitives for the network on downstream tasks.
• Score: 4.451173777061901
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Children acquire language despite being exposed to several orders of magnitude less data than large language models require. Meta-learning has been proposed as a way to integrate human-like learning biases into neural-network architectures, combining both the structured generalizations of symbolic models with the scalability of neural-network models. But what does meta-learning exactly imbue the model with? We investigate the meta-learning of formal languages and find that, contrary to previous claims, meta-trained models are not learning simplicity-based priors when meta-trained on datasets organised around simplicity. Rather, we find evidence that meta-training imprints neural mechanisms (such as counters) into the model, which function like cognitive primitives for the network on downstream tasks. Most surprisingly, we find that meta-training on a single formal language can provide as much improvement to a model as meta-training on 5000 different formal languages, provided that the formal language incentivizes the learning of useful neural mechanisms. Taken together, our findings provide practical implications for efficient meta-learning paradigms and new theoretical insights into linking symbolic theories and neural mechanisms.

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