Improve agent tool-calling with SFT and DPO on SageMaker
💡Learn how to use SFT and DPO on SageMaker to make your AI agents more reliable at calling external tools.
⚡ 30-Second TL;DR
What Changed
Combine SFT and DPO to optimize SLM performance for tool-calling tasks.
Why It Matters
This approach allows developers to build more reliable AI agents by reducing hallucination and improving function-calling precision in resource-constrained environments.
What To Do Next
Follow the tutorial to set up a SageMaker training job using your own tool-calling dataset to benchmark the performance gain of DPO over standard SFT.
Key Points
- •Combine SFT and DPO to optimize SLM performance for tool-calling tasks.
- •Use Amazon SageMaker AI training jobs to offload infrastructure management.
- •Implement systematic evaluation methods to compare base models against fine-tuned variants.
- •Focus on data-driven decision-making for model quality improvements.
Weekly AI Recap
Read this week's curated digest of top AI events →
👉Related Updates
AI-curated news aggregator. All content rights belong to original publishers.
Original source: AWS Machine Learning Blog ↗
This is a summary, not the original. Read the source, or get the weekly briefing.
The weekly digest
One email a week. Unsubscribe anytime.