OpenAI o1 represents a significant milestone in Artificial Inteiligence, which achieves expert-level performances on many challanging tasks that require strong reasoning ability.OpenAI has claimed that the main techinique behinds o1 is the reinforcement learining. Recent works use alternative approaches like knowledge distillation to imitate o1's reasoning style, but their effectiveness is limited by the capability ceiling of the teacher model. Therefore, this paper analyzes the roadmap to achieving o1 from the perspective of reinforcement learning, focusing on four key components: policy initialization, reward design, search, and learning. Policy initialization enables models to develop human-like reasoning behaviors, equipping them with the ability to effectively explore solution spaces for complex problems. Reward design provides dense and effective signals via reward shaping or reward modeling, which is the guidance for both search and learning. Search plays a crucial role in generating high-quality solutions during both training and testing phases, which can produce better solutions with more computation. Learning utilizes the data generated by search for improving policy, which can achieve the better performance with more parameters and more searched data. Existing open-source projects that attempt to reproduce o1 can be seem as a part or a variant of our roadmap. Collectively, these components underscore how learning and search drive o1's advancement, making meaningful contributions to the development of LLM.

翻译：OpenAI o1代表了人工智能领域的一个重要里程碑，它在许多需要强大推理能力的挑战性任务上达到了专家级表现。OpenAI声称o1背后的核心技术是强化学习。近期研究尝试使用知识蒸馏等替代方法模仿o1的推理风格，但其效果受限于教师模型的能力上限。因此，本文从强化学习视角分析实现o1的技术路线，聚焦四个关键组成部分：策略初始化、奖励设计、搜索与学习。策略初始化使模型能够形成类人推理行为，赋予其有效探索复杂问题解空间的能力。奖励设计通过奖励塑形或奖励建模提供密集有效的信号，为搜索和学习提供指导。搜索在训练和测试阶段对生成高质量解方案起着关键作用，通过更多计算可产生更优解。学习利用搜索生成的数据改进策略，通过更多参数和更多搜索数据实现更优性能。现有尝试复现o1的开源项目可视为本路线图的部分实现或变体。这些组成部分共同阐明了学习与搜索如何驱动o1的进步，为大型语言模型的发展做出了实质性贡献。