Reinforcement Learning
A machine learning paradigm where an agent learns optimal behavior by taking actions in an environment and receiving rewards or penalties, without being told the correct actions in advance.
Reinforcement learning produced landmark AI achievements: DeepMind's AlphaGo defeated the world Go champion in 2016, and AlphaStar reached Grandmaster level in StarCraft II. RL is the basis of RLHF, which aligns large language models with human preferences. Applications span robotics (learning to walk and manipulate objects), game playing, autonomous driving, and resource optimization. DeepMind's RL-based data center cooling reduced Google's energy consumption by 40%. The technique requires enormous amounts of trial-and-error interaction, making simulation environments critical.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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