Reinforcement learning is used by both OpenAI and DeepSeek to shape the behavior of their language models based on feedback and reward signals, enhancing their usefulness and alignment.

The biggest change in AI research over the past year is that reinforcement learning (RL) in language models has finally proven effective, demonstrating expert human reliability and performance with the right feedback loop.

Reinforcement learning is integrated into large language models by combining it with human data, which contrasts with the self-learning approach of AlphaZero, and this reinforcement learning from human feedback (RLHF) has transformed large language models (LLMs) from systems that merely mimic data into ones that produce useful answers to questions.

The idea behind reinforcement learning is that you can learn from very sparse rewards, which applies to both language generation and tasks for robots.

The emergence of coherent language models like GPT-3 and GPT-4 was crucial for enabling reinforcement learning from human feedback (RLHF).

Reinforcement learning involves giving a system rewards for successful actions, allowing it to learn which moves lead to winning outcomes over time.

Reinforcement learning on top of models will be critical for building the most powerful AI agents, as it allows for better adaptability and performance in complex tasks.

No matter how a model is designed to behave, the techniques used for reinforcement learning and preference tuning also enhance performance across various tasks.

Reinforcement learning allows us to tackle problems where we don't know the answers, focusing on whether the model's outputs are good or not, rather than needing predefined solutions.