What are RL environments and how to build them

Reinforcement learning (RL) is pivotal in the evolution of AI, transitioning from static data training to dynamic, experience-driven systems. This shift marks the "Era of Experience," where RL must evolve to handle complex, agentic capabilities like multi-step reasoning and tool use. Environments serve as the interactive spaces where models learn by exploring permissible actions and receiving feedback, which is crucial for refining behaviors across trajectories. The blog emphasizes the importance of environments in RL workflows, introducing tools such as Unsloth, NVIDIA NeMo RL, and NeMo Gym to efficiently build and manage these environments. These tools help in decoupling environment logic from training processes, allowing for scalable and flexible RL systems. A hybrid approach often combines Supervised Fine-Tuning (SFT) for initial stages, followed by RL for post-training refinement, as seen with models like NVIDIA Nemotron 3. The rise of RL from Verifiable Rewards (RLVR) highlights a focus on verifiable correctness over subjective scoring, leveraging algorithms like Group Relative Policy Optimization (GRPO) for efficiency. NeMo Gym, in particular, addresses the challenges of building scalable RL environments by providing infrastructure for managing resource lifecycles and standardizing trajectories, which can be integrated with various RL training frameworks to optimize model performance across diverse domains.