Google researchers have achieved a breakthrough that could revolutionize AI. They developed "internal RL," a technique that allows AI models to learn complex reasoning. This advancement bypasses the limitations of traditional next-token prediction. The research, conducted in Google's AI labs, was revealed January 16, 2026.
Internal RL steers a model's internal processes. It guides the AI toward step-by-step solutions. This approach allows AI to handle tasks that typically cause large language models to fail. Current LLMs often hallucinate or struggle with long-term planning.
The immediate impact could be a new generation of AI agents. These agents could perform complex reasoning and control real-world robots. This would reduce the need for constant human oversight. Experts believe this could be a scalable path to autonomous AI.
LLMs currently rely on autoregressive models. These models generate sequences one token at a time. Reinforcement learning is used to refine these models. However, next-token prediction limits their ability to explore new strategies.
Google plans to further develop and test internal RL. The focus will be on expanding its capabilities and real-world applications. The implications for robotics, automation, and AI safety are significant.
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