Papers › o1-Coder: an o1 Replication for Coding

o1-Coder: an o1 Replication for Coding

29 Nov 2024arXiv:2412.00154archive 2025-07-28

Yuxiang Zhang, Shangxi Wu, YuQi Yang, Jiangming Shu, Jinlin Xiao, Chao Kong, Jitao Sang

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 .

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