Organizations struggle to customize AI models for complex tasks due to the extensive human annotation required for traditional fine-tuning approaches. Learn how Amazon Bedrock's new Reinforcement Fine Tuning (RFT) capabilities enable developers to train models using custom evaluators instead of large labeled datasets. Discover how to implement RFT with Amazon Nova and open-source models using techniques like RLVR for code generation and RLAIF for AI feedback. We'll demonstrate how these RL-based customization methods improve model accuracy, safety, and adaptability for enterprise applications while reducing the burden of data annotation.

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