LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structure

2026-06-13

AI Summary

Existing training and evaluation frameworks lack controllable shared latent structures, making it difficult to systematically analyze how agents leverage cross-task experience to improve decision-making. This work proposes LatentGym—the first benchmark suite grounded in real, controllable latent variables—that decouples exploration (acquiring latent knowledge) from exploitation (applying learned knowledge), thereby enabling fine-grained assessment of cross-task adaptation mechanisms. Experiments demonstrate that the platform can uncover the root causes of large language models’ failures in cross-task generalization, validate the efficacy of post-training on task sequences, and elucidate how design choices such as inter-task feedback critically shape learning dynamics and generalization performance.

Abstract

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.

Problem

• cross-task learning
• latent structure
• experiential learning
• agent adaptation
• controllable environment

Innovation

• LatentGym
• cross-task learning
• controllable latent structure
• experiential learning
• exploration-exploitation tradeoff