# Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective

**Authors:** Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, Bo Wang, Shimin Li, Yunhua Zhou, Qipeng Guo, Xuanjing Huang, Xipeng Qiu  
**arXiv:** [2412.14135](https://arxiv.org/abs/2412.14135)  
**Date:** 2024-12-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2412.14135v1/

## Key points

- OpenAI o1 represents a significant milestone in Artificial Intelligence, achieving expert-level performances on challenging tasks that require strong reasoning ability.
- The main technique behind o1 is reinforcement learning, with recent works exploring alternative approaches like knowledge distillation to imitate o1's reasoning style.
- Policy initialization plays a crucial role in developing models with human-like reasoning behaviors, enabling effective exploration of solution spaces for complex problems. This phase includes pre-training, instruction fine-tuning, and the development of human-like reasoning behaviors.
- Techniques like AgentWrite and Self-Lengthen enhance LLMs' long-text generation capabilities.
- Task decomposition is crucial for tackling complex problems by breaking them down into manageable subtasks. Techniques like Compositional Fine-Tuning (CFT) explicitly divide tasks to improve model performance.

## Summary

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OpenAI o1 represents a significant milestone in Artificial Intelligence, achieving expert-level performances on challenging tasks that require strong reasoning ability. The main technique behind o1 is reinforcement learning, with recent works exploring alternative approaches like knowledge distillation to imitate o1's reasoning style. This paper delves into the roadmap to achieving o1 from a reinforcement learning perspective, focusing on key components such as policy initialization, reward design, search, and learning. Policy initialization plays a crucial role in developing models with human-like reasoning behaviors, enabling effective exploration of solution spaces for complex problems. This phase includes pre-training, instruction fine-tuning, and the development of human-like reasoning behaviors. Techniques like AgentWrite and Self-Lengthen enhance LLMs' long-text generation capabilities. Shaping human-like reasoning behaviors logically is vital for models to orchestrate coherent decision-making processes. Exposure to programming code and structured logical data strengthens models' reasoning capabilities. Self-reflection, encompassing self-evaluation, self-correction, and alternative proposal behaviors, addresses limitations of autoregressive models and enhances the model's self-knowledge. Challenges arise in effectively implementing policy initialization for reproducing o1-like models. Task decomposition is crucial for tackling complex problems by breaking them down into manageable subtasks. Techniques like Compositional Fine-Tuning (CFT) explicitly divide tasks to improve model performance. Overall, understanding the intricacies of policy initialization and its impact on model development is essential for advancing AI systems like OpenAI o1. By addressing challenges and leveraging techniques to enhance reasoning abilities and long-text generation capabilities, researchers can further propel the field of Artificial Intelligence towards expert-level performances in various domains.