DivInit: Boosting Agentic Search via Diverse Query Initialization
π‘Learn how to improve agentic search accuracy by 5-7 points without training, using a simple query diversification hack.
β‘ 30-Second TL;DR
What Changed
Standard parallel sampling suffers from query redundancy at the first turn.
Why It Matters
This method provides a cost-effective way to improve agentic search accuracy without requiring additional model training. It offers a practical optimization for developers building RAG-based agent systems.
What To Do Next
Integrate DivInit into your agentic search pipeline by implementing a diversity-based selection step for initial queries to reduce retrieval overlap.
Key Points
- β’Standard parallel sampling suffers from query redundancy at the first turn.
- β’DivInit generates n candidates and selects k diverse seeds to run parallel trajectories.
- β’Achieves 5-7 point gains on multi-hop QA benchmarks at matched compute.
- β’Training-free method compatible with existing open-weight models.
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Original source: ArXiv AI β
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