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AI Research
arXiv CS.AI·1d ago·5 min read
ProgAgent: Learning Tool Orchestration through Programmatic Training
TL;DR
ProgAgent introduces a novel approach to training large language models (LLMs) using Programmatic Tool Calling (PTC), which significantly improves task performance. It achieves a 30.7% pass rate on complex tasks, outperforming traditional methods that scored zero.
✦ Why It Matters
Consider adopting Programmatic Tool Calling in your LLM training to enhance task performance immediately.
Key Takeaways
- Explore Programmatic Tool Calling for improved LLM training.
- Utilize the task synthesis pipeline for diverse training scenarios.
- Implement a two-stage training process for better outcomes.
- Evaluate your models using LOCA-bench to benchmark performance.
How It Works
ProgAgent employs Programmatic Tool Calling (PTC) to invoke tools via executable code, allowing for better management of intermediate results. This method contrasts with Direct Tool Calling (DTC), which accumulates tool responses in the model's context, leading to inefficiencies.
By storing results in runtime variables, PTC mitigates context rot and enhances the model's ability to handle long-horizon tasks effectively.