Conservative Bias in Multi-Teacher AI: Agents Favor Lower-Reward Advisors
Published:Dec 19, 2025 02:38
•1 min read
•ArXiv
Analysis
This ArXiv paper examines a crucial bias in multi-teacher learning systems, highlighting how agents can prioritize less effective advisors. The findings suggest potential limitations in how AI agents learn and make decisions when exposed to multiple sources of guidance.
Key Takeaways
- •Identifies a conservative bias in multi-teacher learning.
- •Agents may not select the most rewarding advisors.
- •Implications for AI agent decision-making and learning efficiency.
Reference
“Agents prefer low-reward advisors.”