Hugging Face has launched a centralized registry for reinforcement learning environments on its Hub platform, addressing a long-standing fragmentation problem in the RL community. The initiative allows developers to discover and run RL tasks across multiple frameworks—including Harbor, Verifiers, OpenEnv, and NVIDIA NeMo Gym—from a single location, eliminating the need for separate custom registries and manual environment porting between systems. The Hub leverages its existing data versioning and hosting infrastructure rather than building new tooling, allowing individual frameworks to supply their own runtime and verifier implementations. Datasets tagged with "rl-environment" now appear in a dedicated filter on the Hub, with framework-specific tags enabling one-click loading. This architecture separates data storage concerns from execution logic, letting the Hub focus on discovery and versioning while frameworks handle agent execution and evaluation. The move reduces friction for RL researchers and practitioners who previously had to maintain environments across multiple isolated ecosystem registries. By consolidating task data on a platform already used by millions, Hugging Face aims to accelerate adoption of reinforcement learning for agent training and evaluation while establishing the Hub as the default discovery layer for RL infrastructure.