Fine-tune reasoning models for specific domains using just 100 examples and reinforcement learning.
OpenRFT enables fine-tuning reasoning models for domain-specific tasks using reinforcement learning with limited data, addressing challenges in reasoning step synthesis and data scarcity.
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https://arxiv.org/abs/2412.16849
Original Problem 🤔:
Current LLMs struggle to generalize reasoning capabilities to domain-specific tasks efficiently. Traditional fine-tuning requires extensive data and often fails to preserve core reasoning abilities.
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Solution in this Paper 🛠️:
→ OpenRFT leverages domain-specific samples through three key mechanisms: question augmentation, reasoning process synthesis, and few-shot in-context learning.
→ The framework employs a Process Reward Model to supervise reasoning quality during reinforcement learning.
→ Data augmentation expands training samples by rephrasing questions and shuffling options.
→ A teacher-student model setup synthesizes intermediate reasoning steps for better adaptation.
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Key Insights 🔍:
→ More domain-specific data consistently improves RFT performance
→ Teacher and student policy models must have aligned action spaces
→ Data augmentation benefits diminish as training data increases
→ Process supervision is crucial for stable reinforcement learning
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Results 📊:
→ Achieved 11% average improvement over baseline using only 100 training samples
→ Best variant (SFT+RL+PRM+DA) consistently outperformed other methods
→ Demonstrated competitive results against stronger models like GPT-4o-mini
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