This week on The Turing Podcast, Anshul Bhagi, Turing’s RL Gym expert, discusses how reinforcement learning environments are built and why they matter right now.
This episode lays out where reinforcement learning fits into the AI stack and how Turing’s RL Gyms are helping elite labs build strength with every cycle.
Highlights:
The two main types of RL environments and how each has evolved
What separates a good RL environment from a great one and why that difference matters for training
When reinforcement learning is the right tool for an AI problem and when it is not
How future RL gyms could simulate entire businesses or train personalized agents in rich virtual environments
To move fast and stay ahead, AI teams need to strengthen their capabilities. Turing’s RL Gyms are designed for that purpose. They are environments where researchers, agents, and systems improve with every iteration. The result is stronger, more capable models and faster progress.
If you are working on complex model training, AGI or ASI development, or building AI-native systems, this episode offers an inside view into the future of AI training infrastructure.
(00:00) Introduction to RL Environments
(04:14) Types of RL Environments
(07:05) Evolution of RL Environments
(09:59) Human Involvement in RL Design
(10:54) When Not to Use RL
(21:40) Accuracy in RL Environments
(24:46) Future of RL Environments
(27:31) Complexity of RL Environments3
(30:37) Future Directions
