Representing expertise accelerates learning from pedagogical interaction data

2026-04-13

AI Summary

This study investigates how teaching interactions between experts and novices enhance learning agent performance compared to pure expert demonstrations. By constructing a synthetic expert-novice interaction dataset in a spatial navigation task and employing a Transformer-based architecture with controlled experimental designs, the work systematically evaluates learning efficacy and generalization under varying data conditions. For the first time within a controlled paradigm, it demonstrates that cognitive discrepancies encoded through multi-agent identities significantly improve model robustness, enabling emergent expert-level performance even when expert behaviors are sparse. The results consistently show that models trained on teaching interaction data outperform baseline approaches relying solely on expert demonstrations across diverse scenarios.

Technology Category

• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: Behavior Learning & Control

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Work in cognitive science and artificial intelligence has suggested that exposing learning agents to traces of interaction between multiple individuals can improve performance in a variety of settings, yet it remains unknown which features of interactions contribute to this improvement. We examined the factors that support the effectiveness of interaction data, using a controlled paradigm that allowed us to precisely operationalize key distinctions between interaction and an expert acting alone. We generated synthetic datasets of simple interactions between an expert and a novice in a spatial navigation task, and then trained transformer models on those datasets, evaluating performance after exposure to different datasets. Our experiments showed that models trained on pedagogical interactions were more robust across a variety of scenarios compared to models trained only on expert demonstrations, and that having the ability to represent epistemically distinct agents led to expert-like behavior even when expert behavior was rarely observed.

Problem

• pedagogical interaction
• expertise representation
• multi-agent interaction
• learning from demonstrations
• epistemic agents

Innovation

• pedagogical interaction
• multi-agent representation
• transformer models
• synthetic interaction data
• epistemic distinction