arXiv:2606.18537v1 Announce Type: new Abstract: Humans often acquire new skills by observing others, since observed behaviors implicitly reveal how to act in an environment. However, observations drawn from a heterogeneous population introduce conflicting behavioral signals, making it difficult to determine which behaviors are worth imitating. We address this challenge with General Reward Inference and Disentanglement (GRID), a social learning method that extracts universally useful behaviors from a heterogeneous population of demonstrators pursuing different goals. GRID decomposes per-agent r
This paper addresses a fundamental challenge in social learning for AI, which is critical as AI agents become more sophisticated and need to learn from diverse human or AI behaviors.
Learning universal behaviors from heterogeneous agents has significant implications for developing more adaptive and robust AI systems capable of operating effectively in complex, multi-actor environments.
Previously, conflicting behavioral signals from diverse sources made robust social learning difficult for AI; this method provides a framework to disentangle and integrate such observations.
- · AI agents developers
- · Robotics
- · Autonomous systems
- · AI research institutions
- · AI models reliant on homogeneous data
- · Approaches requiring extensive supervised learning
AI agents will be able to learn more effectively from diverse human interactions, leading to more generalized skills.
This could accelerate the development of AI agents capable of performing complex tasks with less explicit programming in varied environments.
The widespread deployment of such robust AI agents could fundamentally alter human-computer interaction and reshape industries by automating tasks previously requiring explicit, task-specific training.
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