Iterative Deployment Boosts LLM Planning

This paper highlights a novel training approach for LLMs, demonstrating that iterative deployment and user-curated data can significantly improve planning skills. The connection to implicit reinforcement learning is a key insight, raising both opportunities for improved performance and concerns about AI safety due to the undefined reward function.

• •Iterative deployment of LLMs, with user-curated data, improves planning skills.
• •Later models exhibit emergent generalization, discovering longer plans.
• •The process implicitly implements reinforcement learning with an undefined reward function.
• •This approach offers an alternative to explicit RL, relying on data curation.