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Agent Factory Recap: Reinforcement Learning and Fine-Tuning on TPUs
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Agent Factory Recap: Reinforcement Learning and Fine-Tuning on TPUs

via Dev.toShir Meir Lador

In our agent factory holiday special, Don McCasland and I were joined by Kyle Meggs, Senior Product Manager on the TPU Training Team at Google, to dive deep into the world of model fine tuning. We focused specifically on reinforcement learning (RL), and how Google's own infrastructure of TPUs are designed to power these massive workloads at scale. This post guides you through the key ideas from our conversation. Use it to quickly recap topics or dive deeper into specific segments with links and timestamps. When to Consider Fine-Tuning Timestamp: 3:13 We started with a fundamental question: with foundational models like Gemini becoming so powerful out of the box, and customization through the prompt can often be good enough, when should you consider fine-tuning? Fine tuning your own model is relevant when you need high specialization for unique datasets where a generalist model might not excel (such as in the medical domain), or when you have strict privacy restrictions that require hos

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