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.

Through thousands of reinforcement learning steps, DeepSeek's model learned advanced reasoning skills, including extended chains of thought and the ability to recognize and correct its own mistakes.

DeepSeek's R1 model is one of the first large models to achieve top-tier results purely through reinforcement learning, marking a significant milestone in AI development. Additionally, DeepSeek introduced a cold start phase for fine-tuning on structured reasoning examples before reinforcement learning, which eliminated language mixing issues and made outputs far more comprehensible.

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.

DeepSeek's R1 reasoning model achieves impressive performance by applying reinforcement learning specifically to think step by step through complex problems, similar to OpenAI's approach with their model, o1.

OpenAI has stated that DeepSeek uses their API, and there are claims that it is a distilled model trained on OpenAI's outputs, raising questions about OpenAI's terms of service that prohibit building competitors with outputs from their models.

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.

The performance of AI models improves significantly with reinforcement learning, and OpenAI aims to scale this approach to enhance reasoning capabilities further, moving towards a future where reinforcement learning will dominate AI development and potentially lead to significant advancements in capabilities.

Fine-tuning models like DeepSeek for specific tasks has proven effective, allowing customers to customize their AI solutions to better meet their business needs.