LatentGym benchmarks how LLM agents learn across related tasks
Daksh Mittal, Tommaso Castellani, and Thomson Yen introduce LatentGym, a controllable evaluation suite that measures whether LLM agents can infer hidden structure shared across related tasks and use it to improve future decisions.
Score breakdown
LatentGym fills a gap left by existing frameworks by providing the first controllable latent structure and disentangled exploration/exploitation metrics for measuring cross-task experiential learning in LLM agents.
- 01LatentGym is a controllable benchmark suite for evaluating cross-task experiential learning in LLM agents.
- 02Each environment is organized around a ground-truth latent variable that governs structure across tasks.
- 03The suite produces metrics that separately measure exploration (information gathering about the latent) and exploitation (using gathered information).
Daksh Mittal, Tommaso Castellani, and Thomson Yen present LatentGym, a controllable testbed aimed at a capability they describe as pivotal for personalization and interactive assistance: the ability of an agent to infer hidden structure shared across a sequence of related tasks and use that structure to improve future decisions. Existing training and evaluation frameworks, the paper argues, lack shared, controllable latent structures and cannot measure whether or why agents improve across tasks. LatentGym addresses this by organizing each environment around a ground-truth latent variable, which makes it possible to construct metrics that disentangle exploration — whether the agent's actions gather information about the latent — from exploitation — whether the agent acts on what it has gathered.
The paper reports empirical studies on 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 originate; and how design choices such as inter-task feedback shape training dynamics and generalization. Together, the authors argue, 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.
Key facts
- 01LatentGym is a controllable benchmark suite for evaluating cross-task experiential learning in LLM agents.
- 02Each environment is organized around a ground-truth latent variable that governs structure across tasks.
- 03The suite produces metrics that separately measure exploration (information gathering about the latent) and exploitation (using gathered information).
- 04Empirical studies examine why frontier models fail to adapt across related tasks.
- 05The paper investigates whether post-training on related task sequences improves general cross-task adaptation.
- 06Design choices such as inter-task feedback are studied for their effect on training dynamics and generalization.
- 07Authors are Daksh Mittal, Tommaso Castellani, and Thomson Yen.
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