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Machine Learning Mastery1 Hot0 comments10 min read
Fine-Tuning Agentic AI: A Practical Guide
A practical walkthrough treats agentic AI fine-tuning as four interconnected levers rather than a single step: building a validated tool-calling training dataset, applying QLoRA for parameter-efficient training, tuning runtime hyperparameters like temperature and retry policy, and using Direct Preference Optimization (DPO) to teach judgment calls SFT cannot express. Using a support-ticket triage agent example, it shows how to validate tool-calling examples against schemas, configure QLoRA with r=4/alpha=32/dropout=0.05, add a temperature-0 retry to boost success rate to 98.7%, use DPO pairs to teach nuanced judgment, and build a verdict-driven evaluation function that catches catastrophic forgetting (e.g., flagging HOLD when general capability drops 7.2 points despite a tool-call accuracy jump from 61% to 97%).
Source: https://machinelearningmastery.com/fine-tuning-agentic-ai-a-practical-guide. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.
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