This paper enhances LLMs' physics problem-solving abilities by combining human and AI feedback in reinforcement learning, achieving superior reasoning and accuracy.
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https://arxiv.org/abs/2412.06827
🤔 Original Problem:
LLMs struggle with complex physics reasoning despite showing strong capabilities in text-based tasks. Current approaches using prompt engineering and retrieval augmentation don't fully address reasoning limitations.
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🔧 Solution in this Paper:
→ The paper introduces Reinforcement Learning with Human and AI Feedback (RLHAIF) to improve LLM performance on physics questions
→ RLHAIF combines human and AI feedback to create high-quality preference datasets while minimizing human supervision
→ The solution evaluates multiple reinforcement learning methods including PPO, DPO, and Remax optimization
→ The approach uses the PhyQA dataset containing challenging physics problems from high school textbooks
→ The system incorporates carefully selected few-shot examples of human-ranked physics answers to refine preference data
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💡 Key Insights:
→ Combining human and AI feedback creates more reliable and logically sound outputs
→ PPO outperforms other RL methods for physics reasoning tasks
→ Model struggles most with arithmetic calculations (35% of errors)
→ Problem deduction errors contribute 10% of total errors
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📊 Results:
→ Mistral-PPO achieved 58.67 METEOR score and 0.74 Reasoning score
→ Model demonstrates 64% prediction accuracy on test sets
→ Achieves Sharpe ratio of 2.21 on test portfolio
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