Enterprise Reinforcement Learning Training: Persistent Model Updates on Private Data Without Feeding OpenAI's Next Model
Your company has 50,000 support tickets, 200 internal policy docs, and a compliance team that would rather quit than let you upload anything to an external API. You need an AI agent that learns from corrections, improves over weeks, and remembers what worked last month. Prompt engineering will not save you here. Supervised fine-tuning will not save you here. You need reinforcement learning that runs on your data and outputs a model that belongs to you. Prerequisites Basic understanding of fine-tuning vs training from scratch, awareness that "private data" means actual consequences when leaked, and one uncomfortable truth: most RL tutorials assume you have a Stanford research budget. Read: Ensemble Models: Why, When, and How to Combine Different Machine Learning Families RLHF is not new, but persistent enterprise RLHF is still rare Reinforcement Learning from Human Feedback sounds like every AI hype term mashed together, but the core idea is simple enough: you train a ...
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