Scaling Interactive Learning Resources for LLM Agents: A Survey of Environment and Task Synthesis

Abstract

Large language model (LLM) agents can learn through interaction with external environments, obtain training signals and reusable experience from task execution. Scaling this form of learning requires a sufficient supply of interactive environments and tasks grounded in those environments, with explicit objectives and completion criteria. We refer to these complementary resources as interactive learning resources. This survey reviews their automated construction at scale and continuous adaptation through three interconnected themes: environment synthesis, task synthesis, and learning-driven evolution of both. For environment synthesis, we examine functional components, construction paradigms, and intrinsic quality assurance. For task synthesis, we review intent formation, grounding, completion verification, and task quality assurance. We then examine how a target agent's performance and learning progress inform assessments of resource suitability and guide adaptation to its evolving capabilities and learning needs across successive learning rounds. Finally, we discuss open challenges concerning the order of environment and task synthesis, the trade-off between intrinsic quality and learning utility, long-term resource evolution, and quality assurance at scale.