The potential for AI agents to perform tasks in virtual environments is promising, with a clear path for increasing their capabilities through reinforcement learning.

AI systems can design their own reinforcement learning algorithms, as demonstrated by a project where an AI outperformed human-designed algorithms by learning to optimize its own learning process.

Deep reinforcement learning, which DeepMind is known for, allows systems to learn from scratch by trial and error, achieving superhuman performance in some tasks.

Self-play mechanisms in AI, where systems play against themselves, have led to significant advancements in reinforcement learning, exemplified by systems like AlphaGo and AlphaZero.

The emergence of reasoning behaviors in AI models can occur without human preferences being explicitly added during post-training, showcasing the power of reinforcement learning.

Reinforcement learning on top of models will be critical for building the most powerful AI agents, as it allows for better adaptability and performance in complex tasks.

The performance of AI models improves significantly with reinforcement learning, and OpenAI aims to scale this approach to enhance reasoning capabilities further, moving towards a future where reinforcement learning will dominate AI development and potentially lead to significant advancements in capabilities.

Contemporary machine learning also faces the challenge of building systems that focus on relevant information in the current situation, as demonstrated in projects at DeepMind using Atari games.

While many view AI primarily through the lens of ChatGPT, I believe AI is underhyped because the advancements in reinforcement learning since then have been extraordinary, enabling complex planning capabilities.