LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structure
For researchers studying continual learning and LLM agents, LatentGym offers a controlled framework to evaluate and improve cross-task adaptation, addressing a critical gap in existing benchmarks.
LatentGym provides a controllable testbed for studying cross-task experiential learning in LLM agents, enabling measurement of exploration vs. exploitation. Empirical studies reveal how frontier models fail to adapt across related tasks and how post-training on task sequences improves adaptation.
We envision continually learning agentic systems that become more useful over time: as they encounter sequences of related tasks, they should infer the hidden structure shared across those tasks and use it to improve future decisions. This cross-task experiential learning capability is pivotal in domains such as personalization and interactive assistance, but existing training/evaluation frameworks do not provide shared, controllable latent structures and cannot measure whether or why agents improve. We introduce LatentGym: a controllable suite in which each environment is organized around a ground-truth latent variable governing the structure across tasks. Our construction yields metrics that separate exploration (whether the agent's actions gather information about the latent) from exploitation (whether the agent uses what it has gathered). We demonstrate our suite on empirical studies addressing three questions: how and why frontier models fail to adapt across related tasks; whether post-training on related task sequences improves general cross-task adaptation, and where those gains come from; and how design choices such as inter-task feedback shape training dynamics and generalization. Together, these results establish a controlled foundation for studying how LLM agents learn from experience across tasks, and for designing agents that adapt more reliably in sequential, personalized, and interactive settings.