Mark (@markkmii)'s insight that simulations alone are not enough even for short-term tasks, and performance improves only when failure cases are also learned. He emphasizes that while simulations help 'guess' the task, real data teaches the simulator where it went wrong. Agent/Robot/Learning Loop
A new insight suggests that for AI agents to improve performance, they need to learn from real-world failure cases, not just simulations. While simulations can help agents succeed at tasks, actual data is crucial for teaching the simulator where it went wrong. This approach is particularly relevant for agent/robot learning loops. AI
IMPACT Highlights the need for real-world data to refine AI agent training beyond purely simulated environments.