Learning to Coordinate Through Strategic Game Slices

Coordination under private information requires agents to infer partners’ preferences and select actions using those inferences. We study these capabilities in a finite negotiation game with private preferences and mixed incentives. A reference-policy oracle supplies posterior judgments and counterfactual action values, enabling matched diagnostic interventions. For Qwen3-4B-Instruct-2507, these probes reveal errors in both preference inference and action selection: supplying an oracle judgment does not reliably eliminate action errors. We use the same oracle to construct game slices and train a shared model on direct action selection, preference inference, and planning. With approximately 6M generated response tokens, joint slice training retains 47 of 72 meetings in a 24-game, four-agent sequential calendar suite adapted from CalBench, compared with 37 for both the unchanged model and complete-game self-play. These results provide initial evidence that targeted supervision from controlled strategic games can improve coordination in an environment with different interaction mechanics.