Add benefits to About page aligned with README

README.md

@@ -9,9 +9,9 @@ NeMo Gym is a component of the [NVIDIA NeMo Framework](https://docs.nvidia.com/n
 
 - Scaffolding and patterns to accelerate environment development: multi-step, multi-turn, and user modeling scenarios
 - Contribute environments without expert knowledge of the entire RL training loop
-- Test environment and throughput end-to-end independent of the RL training loop
-- Interoperable with existing environments, systems and RL training frameworks
-- Growing collection of training environments and datasets to enable Reinforcement Learning from Verifiable Reward (RLVR)
+- Test environments and throughput end-to-end, independent of the RL training loop
+- Interoperable with existing environments, systems, and RL training frameworks
+- Growing collection of training environments and datasets for Reinforcement Learning from Verifiable Reward (RLVR)
 
 > [!IMPORTANT]
 > NeMo Gym is currently in early development. You should expect evolving APIs, incomplete documentation, and occasional bugs. We welcome contributions and feedback - for any changes, please open an issue first to kick off discussion!

docs/about/index.md

@@ -22,6 +22,12 @@ Embedding custom training environments directly within training frameworks is co
 
 [NeMo Gym](https://github.com/NVIDIA-NeMo/Gym) decouples environment development from training, letting you build and iterate on environments independently. It provides the infrastructure to develop agentic training environments and scale rollout collection, enabling seamless integration with your preferred training framework.
 
+- Scaffolding and patterns to accelerate environment development: multi-step, multi-turn, and user modeling scenarios
+- Contribute environments without expert knowledge of the entire RL training loop
+- Test environments and throughput end-to-end, independent of the RL training loop
+- Interoperable with existing environments, systems, and RL training frameworks
+- Growing collection of training environments and datasets for Reinforcement Learning from Verifiable Reward (RLVR)
+
 ## Core Components
 
 A training environment consists of three server components: