Reproducing OpenAI's o1: A Roadmap from a Reinforcement Learning Perspective
2025-01-03
A new paper explores the path to reproducing OpenAI's enigmatic model, o1, from a reinforcement learning perspective. Researchers argue o1's powerful reasoning isn't due to a single technique, but rather the synergy of four key components: policy initialization, reward design, search, and learning. Policy initialization equips the model with human-like reasoning; reward design provides dense and effective signals guiding search and learning; search generates high-quality solutions during training and testing; learning utilizes data from search to improve the policy, ultimately achieving better performance. This paper offers valuable insights into understanding and reproducing o1, providing new avenues for LLM development.