Mappa: Fine-Tune Multi-Agent LLMs with AI Coaches
Sonic Intelligence
The Gist
Mappa uses an external LLM coach (e.g., Gemini) to assign per-action scores, improving multi-agent LLM training.
Explain Like I'm Five
"Imagine you have a team of toy robots, and a smart teacher tells each robot what it did right or wrong, so they learn to work together better!"
Deep Intelligence Analysis
Impact Assessment
Mappa addresses the challenge of training multi-agent LLM systems by providing dense training signals without ground truth labels. This approach could lead to more effective and efficient multi-agent AI systems.
Read Full Story on NewsKey Details
- ● Mappa uses an external LLM to score individual agent actions.
- ● Tested with Qwen and LLaMA base models.
- ● Achieved +17pp on AIME math competition.
- ● Achieved +38% F1 on Kaggle-style data science tasks.
Optimistic Outlook
The framework's generality allows for customization with different agents, tasks, and coach models. The ability to run trained models offline reduces reliance on API calls and cloud resources.
Pessimistic Outlook
The hardware requirements (2-8x 80GB GPUs) may limit accessibility for some researchers and developers. The reliance on an external LLM coach during training could introduce bias or limitations.
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