P^2O: Joint Policy and Prompt Optimization
📰 ArXiv cs.AI
P^2O optimizes policies and prompts jointly for more efficient reinforcement learning in Large Language Models
Action Steps
- Identify hard samples that yield near-zero success rates
- Apply joint policy and prompt optimization to improve exploration efficiency
- Use verifiable rewards to enhance reasoning capabilities of LLMs
- Evaluate the performance of P^2O using zero-advantage estimates
Who Needs to Know This
AI engineers and ML researchers can benefit from P^2O to improve the performance of LLMs, especially when dealing with hard samples
Key Insight
💡 Joint optimization of policies and prompts can improve the efficiency of reinforcement learning in LLMs
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🤖 Joint policy & prompt optimization for LLMs! 🚀
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