From Imitation to Understanding: Emergent Expert Modelling in Social Learning

Recent work has demonstrated that artificial agents can develop emergent social learning, using observations of expert agents to solve otherwise difficult tasks without an explicit imitation objective. In humans, such imitation is strongly linked to Theory of Mind: the ability to model the intentions, mental states, and behaviours of others. Motivated by an information-theoretic analysis of the information offered by the expert, we study whether emergent social learning analogously induces expert-modelling capabilities: the representation of the expert's latent state, goal, and behaviours. We study this question across four qualitatively different environments covering grid-worlds (GoalCycle, Overcooked), continuous control (Brax Ant), and an abstract combinatorial task (Travelling Salesman). We then test this relationship empirically and report three findings. First, social learning tracks how informative the expert is: manipulations that make the expert redundant (e.g. revealing the goal to the learner) collapse social learning and expert modelling together. Secondly, without manipulations and across independently trained agents, stronger social learning is correlated with stronger expert modelling in almost every setting. Third, the modelling view suggests concrete interventions in the form of representation-learning objectives and curiosity-style rewards that lead to large increases in both social learning and expert modelling. Together, these findings suggest a strong developmental link between social learning and modelling others.