To understand the emergence of grammatical behavior in language models, we track circuits for subject–verb number agreement in pythia-1b and ten seeds of pythia-410m across training. We find that subject–verb agreement is learned early, and is initially handled by a diffuse collection of neurons. However, after agreement *behavior* has stabilized, the *mechanisms* responsible for it continue to reorganize. The attention heads which mediate agreement often shift during training: in the case of pythia-1b, behavior is controlled by two heads early in training but by a third (different) head later. Furthermore, the circuit shrinks over training, with pythia-1b’s final circuit being strikingly sparse. We take these results to be informative about how linguistic knowledge emerges and is represented in a language model.

Language Models (LMs) have emerged as powerful sources of evidence for linguists seeking to develop theories of syntax. In this paper, we argue that causal interpretability methods, applied to LMs, can greatly enhance the value of such evidence by helping us characterize the abstract mechanisms that LMs learn to use. Our empirical focus is a set of English filler–gap dependency constructions (e.g., questions, relative clauses). Linguistic theories largely agree that these constructions share many properties. Using experiments based in Distributed Interchange Interventions, we show that LMs converge on similar abstract analyses of these constructions. These analyses also reveal previously overlooked factors – relating to frequency, filler type, and surrounding context – that could motivate changes to standard linguistic theory. Overall, these results suggest that mechanistic, internal analyses of LMs can push linguistic theory forward.