Google researchers have developed a new AI technique, internal reinforcement learning (internal RL). It could pave the way for more capable AI agents. The breakthrough, announced January 16, 2026, addresses limitations in how LLMs learn complex reasoning.
Internal RL steers a model's internal processes. This helps it develop step-by-step solutions. Current LLMs struggle with long-horizon planning due to their token-by-token generation. This new method bypasses the need for constant human oversight.
The immediate impact could be seen in robotics and autonomous systems. Experts believe this advance will lead to AI that can handle complex tasks more independently. The development marks a significant step beyond next-token prediction.
LLMs are typically trained using next-token prediction. This method forces models to make small, random changes. Internal RL offers a more direct approach to complex problem-solving.
Researchers plan to explore applications in real-world scenarios. The focus will be on scaling the technology for broader use. This could revolutionize AI's role in various industries.
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