---
title: "Improve your agent’s tool-calling accuracy with SFT and DPO on Amazon SageMaker AI"
slug: "improve-your-agents-tool-calling-accuracy-with-sft-and-dpo-on-amazon-sagemaker-ai"
date: 2026-06-03
category: tech-pub
tags: [agents, amazon]
language: en
sources_count: 1
featured: false
publisher: AInauten News
url: https://news.ainauten.com/en/story/improve-your-agents-tool-calling-accuracy-with-sft-and-dpo-on-amazon-sagemaker-ai
---

# Improve your agent’s tool-calling accuracy with SFT and DPO on Amazon SageMaker AI

**Published**: 2026-06-03 | **Category**: tech-pub | **Sources**: 1

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## TL;DR

In this post, you learn how to use Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) together to improve the tool-calling accuracy of a small language model (SLM).

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## Summary

In this post, you learn how to use Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) together to improve the tool-calling accuracy of a small language model (SLM). The example uses Amazon SageMaker AI training jobs, so you can focus on training code instead of managing your own training infrastructure. You also learn how to evaluate tool-calling accuracy and compare a base model to several fine-tuned variants, so you can make data-driven decisions about model quality.

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## Why it matters

In this post, you learn how to use Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) together to improve the tool-calling accuracy of a small language model (SLM).

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## Key Points

- In this post, you learn how to use Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) together to improve the tool-calling accuracy of a small language model (SLM).
- The example uses Amazon SageMaker AI training jobs, so you can focus on training code instead of managing your own training infrastructure.
- You also learn how to evaluate tool-calling accuracy and compare a base model to several fine-tuned variants, so you can make data-driven decisions about model quality.

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## Nauti's Take

The upside: tuning small models with SFT and DPO so they call tools reliably can cut cost and latency versus large LLMs. The catch: the effort for training data, evaluation and pipelines is real, and without a clean dataset the fine-tuning yields little. Practically, the approach pays off mostly for teams with well-defined tool workflows that want measurable accuracy gains.

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## FAQ

**Q:** What is Improve your agent’s tool-calling accuracy with SFT and DPO on Amazon SageMaker AI about?

**A:** In this post, you learn how to use Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) together to improve the tool-calling accuracy of a small language model (SLM).

**Q:** Why does it matter?

**A:** In this post, you learn how to use Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) together to improve the tool-calling accuracy of a small language model (SLM).

**Q:** What are the key takeaways?

**A:** In this post, you learn how to use Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) together to improve the tool-calling accuracy of a small language model (SLM).. The example uses Amazon SageMaker AI training jobs, so you can focus on training code instead of managing your own training infrastructure.. You also learn how to evaluate tool-calling accuracy and compare a base model to several fine-tuned variants, so you can make data-driven decisions about model quality.

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## Related Topics

- [agents](https://news.ainauten.com/en/tag/agents)
- [amazon](https://news.ainauten.com/en/tag/amazon)

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## Sources

- [Improve your agent’s tool-calling accuracy with SFT and DPO on Amazon SageMaker AI](https://aws.amazon.com/blogs/machine-learning/improve-your-agents-tool-calling-accuracy-with-sft-and-dpo-on-amazon-sagemaker-ai/) - AWS Machine Learning Blog

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## About This Article

This article is a synthesis of 1 sources, curated and summarized by AInauten News. We aggregate AI news from trusted sources and provide bilingual (German/English) coverage.

**Publisher**: [AInauten](https://www.ainauten.com) | **Site**: [news.ainauten.com](https://news.ainauten.com)

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*Last Updated: 2026-06-03*