Reinforcement Fine-Tuning and the Future of Specialized AI Models
Data Brew by Databricks
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Challenges in Reinforcement Fine-Tuning
This chapter examines the intricacies of reinforcement fine-tuning in AI, highlighting the risks of reward hacking and the need for effective reward function design. It emphasizes structured learning approaches and the importance of continuous adjustment and monitoring to enhance model performance and avoid unintended shortcuts.
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