o1-Coder: an o1 Replication for Coding

The technical report introduces O1-CODER, an attempt to replicate OpenAI's o1 model with a focus on coding tasks. It integrates reinforcement learning (RL) and Monte Carlo Tree Search (MCTS) to enhance the model's System-2 thinking capabilities. The framework includes training a Test Case Generator (TCG) for standardized code testing, using MCTS to generate code data with reasoning processes, and iteratively fine-tuning the policy model to initially produce pseudocode and then generate the full code. The report also addresses the opportunities and challenges in deploying o1-like models in real-world applications, suggesting transitioning to the System-2 paradigm and highlighting the imperative for world model construction. Updated model progress and experimental results will be reported in subsequent versions. OpenAI recently introduced the o1 model (OpenAI, 2024), which has demonstrated impressive system-2 thinking capabilities. This model represents a significant advancement in AI's ability to perform complex reasoning tasks that require higher-order cognitive functions. Following its release, numerous analysis and replication efforts have emerged, demonstrating the growing interest in reasoning models. Prior to the o1 model, large language models (LLMs) primarily exhibited System-1 capabilities, characterized by fast, intuitive responses.