REL helps LLMs learn reasoning by watching humans solve problems
This paper introduces Reasoning Enhancement Loop (REL), a method to improve LLMs' reasoning abilities through high-quality worked solutions.
By combining human expertise with AI assistance, they create detailed problem-solving demonstrations that capture authentic reasoning processes, achieving significant performance improvements with minimal training data.
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https://arxiv.org/abs/2412.04645
🤔 Original Problem:
Current LLMs struggle with complex reasoning tasks because their training data lacks detailed problem-solving processes, containing mostly final solutions rather than step-by-step reasoning.
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🔧 Solution in this Paper:
→ The paper introduces REL, combining human expertise with AI to generate high-quality worked solutions.
→ They first create a dataset of human-annotated mathematical solutions, capturing authentic problem-solving processes.
→ A generator model is fine-tuned on these solutions to learn reasoning patterns.
→ A verifier model identifies errors and provides hints for correction.
→ The process iteratively refines solutions through a hint-based correction system.
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💡 Key Insights:
→ Quality of reasoning demonstrations matters more than quantity
→ Small amounts of expert-crafted data can unlock latent model capabilities
→ Fundamental problem-solving strategies trace back to human demonstrations
→ Sophisticated reasoning behaviors can be induced in smaller models
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📊 Results:
→ REL achieved 27.78% accuracy vs 12% baseline on AIME problems
→ Models trained on 100 human-annotated solutions (18.89%) outperformed those trained on 1000 standard solutions (5.56%)
→ Released O1-Llama 3.2 3B demonstrates reasoning capabilities in smaller models
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