Technical architecture of Gemini: Transformer models and reinforcement learning

Google Gemini’s technical architecture is built on advanced deep learning techniques, combining Transformer models and reinforcement learning to enable powerful multimodal AI capabilities.

Transformer Models in Gemini

• Gemini is fundamentally based on the Transformer decoder architecture, a neural network design introduced by Google in 2017 and widely used in large language models (LLMs).
• The model uses multi-head self-attention mechanisms to process and relate tokens in input sequences, allowing it to capture complex contextual relationships across text, images, audio, and video.
• Gemini supports interleaved multimodal inputs (text, images, audio, video) and can generate interleaved outputs, making it highly versatile.
• The architecture is optimized for large-scale, stable training and efficient inference, leveraging Google’s Tensor Processing Units (TPUs) for accelerated performance.
• Gemini 1.5 and later versions incorporate Mixture-of-Experts (MoE) architecture, where the model is divided into smaller, specialized expert networks. Only the most relevant experts are activated for a given input, improving efficiency and scalability.
• The model supports a 32k context length, enabling it to process long sequences of information.

Reinforcement Learning in Gemini

• Gemini uses reinforcement learning to improve its reasoning and problem-solving capabilities, especially for complex tasks.
• The Deep Think feature allows Gemini to explore multiple reasoning strategies, refine its approach, and make step-by-step improvements, similar to how reinforcement learning agents learn through trial and feedback.
• This approach helps Gemini tackle tasks that require creativity, strategic planning, and iterative refinement, such as coding challenges and scientific problem-solving.
• Reinforcement learning is also used to calibrate the model’s thinking process based on task complexity, optimizing resource usage and output quality.

Summary

• Core Architecture: Transformer decoder with MoE enhancements.
• Multimodal Support: Processes and generates text, images, audio, and video.
• Efficiency: Optimized for scale and speed using TPUs and MoE.
• Reasoning: Enhanced with reinforcement learning for complex, iterative tasks.

Gemini’s architecture represents a significant advancement in multimodal AI, combining the strengths of Transformer models and reinforcement learning to deliver real-time, scalable, and highly capable AI experiences.