Rapid progress in machine learning for natural language processing has the potential to transform debates about how humans learn language. However, the learning environments and biases of current artificial learners and humans diverge in ways that weaken the impact of the evidence obtained from learning simulations. For example, today's most effective neural language models are trained on roughly one thousand times the amount of linguistic data available to a typical child. To increase the relevance of learnability results from computational models, we need to train model learners without significant advantages over humans. If an appropriate model successfully acquires some target linguistic knowledge, it can provide a proof of concept that the target is learnable in a hypothesized human learning scenario. Plausible model learners will enable us to carry out experimental manipulations to make causal inferences about variables in the learning environment, and to rigorously test poverty-of-the-stimulus-style claims arguing for innate linguistic knowledge in humans on the basis of speculations about learnability. Comparable experiments will never be possible with human subjects due to practical and ethical considerations, making model learners an indispensable resource. So far, attempts to deprive current models of unfair advantages obtain sub-human results for key grammatical behaviors such as acceptability judgments. But before we can justifiably conclude that language learning requires more prior domain-specific knowledge than current models possess, we must first explore non-linguistic inputs in the form of multimodal stimuli and multi-agent interaction as ways to make our learners more efficient at learning from limited linguistic input. For example, today’s most effective neural language models are trained on roughly one thousand times the amount of linguistic data available to a typical child. To increase the relevance of learnability results from computational models, we need to train model learners without significant advantages over humans. If an appropriate model successfully acquires some target linguistic knowledge, it can provide a proof of concept that the target is learnable in a hypothesized human learning scenario. While this example is idealized, we believe it is generally far more practical to aim for model learners that lack any unfair advantages over humans, than to try to equip models with the full richness of the hypothesized human learning scenario. Ablation experiments can provide a rigorous test for claims common in the language acquisition literature that the input to the learner lacks key evidence for acquiring certain forms of linguistic knowledge ( Chomsky 1971 ; Legate and Yang 2002 ; Lidz et al. 2003 ; Berwick et al. 2011 ; Rasin and Aravind 2021 ) . From this perspective, not all models or results are created equal. We recommend a strategy in which relatively impoverished model learners are used to obtain proofs of concept for learnability. We justify this recommendation through theoretical considerations about the conditions under which results generalize from models to humans. 2.1 Generalizing Learnability Results from Models to Humans One goal of the study of language acquisition is to determine the necessary and sufficient conditions for language learning in humans. However, there are some things we cannot easily learn just by observing humans. If we find that our impoverished models fail, the next course of action is to test whether this is really due to the ablation by enriching the model scenario in innocent ways. This could involve adding to the model scenario sensorimotor input, interaction, and other advantages that humans enjoy. 2.3 Applying Ablations to Debates in Language Acquisition The literature on language acquisition has centered around the necessity and sufficiency of innate advantages and environmental advantages. Model results are averages over three 100M word miniBERTas reported in Zhang et al. 2021 . Most popular ANNs for NLP have been trained on far more words than a human learner. While this was not the case only a few years ago, this trend has only been increasing. Thus, researchers interested in questions about human language acquisition have already begun to intentionally shift their focus to evaluating models trained on more human-scale datasets ( 5.2.1 Misconception 1: A Good Model Learner Must be Unbiased One possible misconception is a model learner must be an unbiased tabula rasa in order to prove some innate bias unnecessary for language acquisition. First, this would be an impossible standard to meet, since all learners have some inductive bias. An inductive bias is just a prior over the hypothesis space, and thus a necessary property of any learner ( Mitchell 1980 ) . Second, we know of no claims that humans are totally unbiased learners. 2020b . Parallelization can make this even faster. Very little hands-on work is required during this training period. Experimentation on artificial learners comes with few ethical restrictions. This is in contrast to experiments on human subjects—and especially infants—which must present minimal risk of harm to the subject. By design, ablations are often very harmful to learning outcomes, meaning we can never do an ablation on L1 acquisition in humans. Aside from experimentation on artificial language learning in humans, the only acquisition ablations we can do on language acquisition is in model learners. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) , pages 2227–2237. Association for Computational Linguistics, 2018. doi: 10.18653/v1/N18-1202 . URL http://aclweb.org/anthology/N18-1202 . Petroni et al. (2019) Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. Language Models as Knowledge Bases?