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About this paper
Augusto B. Corrêa, Yoav Gelberg, Luckeciano C. Melo, Ilia Shumailov, André G. Pereira, Yarin Gal
Researchers demonstrated that iteratively deploying Large Language Models and fine-tuning them on user-curated data from previous generations can more than double their planning capabilities across various domains, leading to emergent generalization for longer-horizon tasks. This process is formally equivalent to reinforcement learning with implicit rewards, highlighting significant AI safety and alignment challenges.
Key insights
- Iterative deployment combined with a data curation step (filtering for valid and high-quality solutions) serves as a potent self-improvement mechanism for LLMs' planning abilities.
- This iterative Supervised Fine-Tuning on curated valid traces functions as an implicit Reinforcement Learning process, specifically a form of REINFORCE.
- The implicit and uncontrolled nature of the 'reward function' derived from real-world user interactions and data curation introduces significant AI safety and alignment risks.
Summary by alphaXiv.
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