
Google's Internal RL: A Leap for Long-Horizon AI Agents
Google's "internal RL" technique offers a promising alternative to traditional next-token prediction for training AI models, potentially enabling them to tackle complex reasoning tasks and long-horizon planning more effectively. By steering the model's internal activations toward step-by-step solutions, this approach could pave the way for creating more autonomous AI agents capable of handling real-world robotics and complex problem-solving without constant human intervention. This development addresses the limitations of current LLMs, which struggle with long-horizon tasks due to their token-by-token approach.


















Discussion
Join the conversation
Be the first to comment