Step-by-step blueprint reveals how to recreate OpenAI's o1's reasoning abilities from scratch.
This paper presents a roadmap to reproduce OpenAI's o1 model using reinforcement learning, focusing on policy initialization, reward design, search, and learning components.
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https://arxiv.org/abs/2412.14135
🤔 Original Problem:
→ Current attempts to replicate o1's capabilities through knowledge distillation are limited by teacher model capabilities. A systematic approach using reinforcement learning is needed.
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🔧 Solution in this Paper:
→ The roadmap establishes basic language understanding through pre-training and develops human-like reasoning through instruction fine-tuning.
→ It implements reward shaping and modeling to transform sparse rewards into dense signals for both search and learning phases.
→ The solution scales both training computation through reinforcement learning and inference computation through "thinking time."
→ It employs tree search methods and sequential revisions to generate high-quality solutions during training and testing.
→ The framework utilizes data generated by search to improve policy through reinforcement learning.
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💡 Key Insights:
→ Policy initialization through pre-training and instruction fine-tuning is crucial for effective exploration
→ Dense reward signals via reward shaping improve both search and learning efficiency
→ Combining tree search with sequential revisions produces better solutions
→ Scaling both training and inference computation leads to consistent performance gains
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📊 Results:
→ The model achieves expert-level performance on complex reasoning tasks
→ Performance consistently improves with increased computation during both training and inference
→ The framework successfully reproduces o1's human-like reasoning behaviors
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