State-Grounded Multi-Agent Synthetic Data Generation for Tool-Augmented LLMs
Learn to generate synthetic data for tool-augmented LLMs using StateGen, a platform that produces scored conversations by orchestrating a four-role LLM loop, and apply it to improve LLM training
- Implement StateGen to generate synthetic conversational data
- Configure the four-role LLM loop to simulate user interactions
- Train tool-augmented LLM agents using the generated data
- Evaluate the performance of the LLM agents using scoring metrics
- Refine the StateGen platform to adapt to specific use cases
NLP engineers and researchers working on LLMs can benefit from this technique to generate high-quality training data, while data scientists and product managers can apply this to improve LLM-based product performance
💡 StateGen can generate high-quality, scored conversational data for tool-augmented LLMs, reducing the need for expensive annotation and improving model performance
🤖 Generate synthetic data for tool-augmented LLMs with StateGen! 📊 Improve LLM training and performance with scored conversations 🚀
Key Takeaways
Learn to generate synthetic data for tool-augmented LLMs using StateGen, a platform that produces scored conversations by orchestrating a four-role LLM loop, and apply it to improve LLM training
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Abstract:
arXiv:2606.16307v1 Announce Type: new Abstract: Training tool-augmented LLM agents requires large corpora of multi-turn, tool-grounded conversational data that is expensive to annotate, privacy-constrained in production settings, and largely absent from public datasets. We present StateGen, a synthetic data generation platform that produces scored, reasoning-trace-rich training conversations by orchestrating a four-role LLM loop: a persona-conditioned user simulator, an agent under test, a state
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