# Comment on "Modeling rapid language learning by distilling Bayesian priors into artificial neural networks"

arXiv: https://arxiv.org/abs/2608.12974 · HTML: https://mlsanity.com/paper/2608.12974/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 9 references that are in the map.
- First posted on arXiv 13 Aug 2026.

## Builds on (most cited first, 7 of 7)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Recasting Gradient-Based Meta-Learning as Hierarchical Bayes](https://mlsanity.com/paper/1801.08930/) (2018)
- [Modeling rapid language learning by distilling Bayesian priors into artificial neural networks](https://mlsanity.com/paper/2305.14701/) (2023)
- [One Model for the Learning of Language](https://mlsanity.com/paper/1711.06301/) (2017)
- [Bridging the Empirical-Theoretical Gap in Neural Network Formal Language Learning Using Minimum Description Length](https://mlsanity.com/paper/2402.10013/) (2024)
- [Meta-Learning Neural Mechanisms rather than Bayesian Priors](https://mlsanity.com/paper/2503.16048/) (2025)
- [A Minimum Description Length Approach to Regularization in Neural Networks](https://mlsanity.com/paper/2505.13398/) (2025)

## Abstract (arXiv)

McCoy & Griffiths (2025, henceforth M&G) suggest that a Bayesian prior can be distilled into Artificial Neural Networks (ANNs) through Model-Agnostic Meta-Learning (MAML, Finn et al., 2017). They support this empirically by showing that meta-trained networks demonstrate formal language learning abilities comparable to Yang & Piantadosi (2023)'s Bayesian learner, significantly outperforming standard ANNs. We point out that under the standard interpretation of a prior, M&G's procedure does not actually instill one; it merely initializes network weights favorably, leaving the objective function unchanged. We then consider a more permissive interpretation, where the system as a whole can be seen as implementing a Bayesian learner even without an explicit prior in the objective. We show that this interpretation faces nontrivial challenges. Finally, we assess how well MAML approximates the empirical results of Bayesian learning, showing that unlike genuine Bayesian learners, M&G's model overfits and generalizes poorly to unseen data.