Agentic data science with the Data Agent Kit
cloud.google.com·8h ago
AI Research
arXiv CS.AI·Sep 1·5 min read
LiteSearch-VL: Small Multimodal Search Agents via Trajectory Distillation and Synthetic Step-DPO
TL;DR
LiteSearch-VL introduces a low-compute method for creating small multimodal search agents that effectively answer visual questions. The approach demonstrates significant behavioral improvements, achieving a 28.4% macro Pass@1 accuracy with a 2B model, rivaling larger models.
✦ Why It Matters
Consider implementing LiteSearch-VL techniques to enhance your multimodal search applications today.
Key Takeaways
- Explore LiteSearch-VL for efficient multimodal search solutions.
- Utilize OpenSearch-VL trajectories for model training.
- Implement LoRA adapters for parameter efficiency.
- Focus on answer verification to improve model accuracy.
How It Works
LiteSearch-VL distills agent trajectories from larger models into smaller ones by leveraging released data and synthetic preferences. It employs Direct Preference Optimization (DPO) to refine the model's decision-making process, targeting specific failure modes identified in previous models.