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

We find that human feedback is often unreliable, so our self-evolving models are designed to learn from their own mistakes without needing human input.

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.

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.

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.

The emergence of reasoning behaviors in AI models can occur without human preferences being explicitly added during post-training, showcasing the power of reinforcement learning.

Removing human feedback from training may lead to models that are not grounded, as they would not learn from the actual consequences of their actions in the world.

In AlphaZero, we found that removing human data allowed the system to learn more effectively, achieving higher performance than models that relied on human knowledge.

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