From Prompts to Policies: How RL Builds Better AI Agents with Mahesh Sathiamoorthy - #731
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Fine-Tuning AI with Reinforcement Learning
This chapter explores the fine-tuning of AI models, emphasizing the use of reinforcement learning (RL) to adapt models for specific enterprise tasks. It highlights the advantages of RL over traditional supervised fine-tuning, such as reduced data requirements and efficient training processes. The discussion also covers the balance of reward shaping and practical applications of RL in evaluating AI actions, providing insights into the evolution of AI training methodologies.
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