Reinforcement learning is experiencing a resurgence due to advancements in language models, making it ripe for tuning to specific use cases with defined reward functions.

There's surprising evidence that once you set up ways to collect verifiable data, language models can learn from sparse rewards, even if they are very weak models.

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.

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

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.

Even small models can boost their performance in tasks like math with a small amount of reinforcement learning training.

Deep reinforcement learning, which DeepMind is known for, allows systems to learn from scratch by trial and error, achieving superhuman performance in some tasks.

Reinforcement learning from verifiable rewards has significantly improved AI capabilities, especially in software engineering, where tasks can be verified easily through tests and outputs.

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