Multiparty tutoring policies must regulate turn-taking and social attention, yet collecting the interaction data needed for reinforcement learning is expensive in educational settings. We introduce an LLM-based user simulator for triadic robot–student dialogue that represents gaze and gesture as inline stage directions. Combined with phase-tagged prompting (which delineates conversational structure) and per-turn persona reinforcement, this design allows the LLMs to generate interactions that preserve key discourse structure and controllable participation dynamics. We validate six simulator variants against a real student–robot corpus (12 student pairs) using metrics for semantic similarity, cue obedience, and persona consistency. While the simulator reaches strong semantic agreement with real transcripts, it also reveals a measurable sim-to-real style gap (in verbosity and grammatical complexity). The simulator enables the creation of an RL environment where the tutor selects factorized gaze and reference actions, learning to interact with simulated students. We compare online training (APPO) to offline learning (DQN). Off-policy evaluation on real logs shows that offline policies trained purely from synthetic data exceed online-trained policies under a doubly-robust estimator, and a verbal-only ablation yields unstable OPE due to heavy-tailed importance weights. These results support a scalable workflow for prototyping socially grounded multiparty tutoring policies while relaying where real-world coverage and OPE reliability remain bottlenecks.

Investigating cooperativity of interlocutors is central in studying pragmatics of dialogue. Models of conversation that only assume cooperative agents fail to explain the dynamics of strategic conversations. Thus, we investigate the ability of agents to identify non-cooperative interlocutors while completing a concurrent visual-dialogue task. Within this novel setting, we study the optimality of communication strategies for achieving this multi-task objective. We use the tools of learning theory to develop a theoretical model for identifying non-cooperative interlocutors and apply this theory to analyze different communication strategies. We also introduce a corpus of non-cooperative conversations about images in the GuessWhat?! dataset proposed by De Vries et al. (2017). We use reinforcement learning to implement multiple communication strategies in this context and find that empirical results validate our theory.