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. All source code, curated datasets, as well as the derived models are disclosed at https://github.com/ADaM-BJTU/O1-CODER .

翻译：本技术报告介绍了O1-CODER，这是对OpenAI o1模型在代码任务上的复现尝试。该模型整合了强化学习（RL）与蒙特卡洛树搜索（MCTS），以增强模型的系统二思维能力。框架包含训练用于标准化代码测试的测试用例生成器（TCG）、使用MCTS生成带推理过程的代码数据，以及通过迭代微调策略模型使其首先生成伪代码再生成完整代码。报告还探讨了在实际应用中部署类o1模型的机遇与挑战，建议向系统二范式过渡，并强调了构建世界模型的必要性。更新的模型进展与实验结果将在后续版本中报告。所有源代码、精选数据集及衍生模型均公开于https://github.com/ADaM-BJTU/O1-CODER。