LiteSearch-VL: Small Multimodal Search Agents via Trajectory Distillation and Synthetic Step-DPO

LiteSearch‑VL demonstrates that distilling released agent trajectories into small vision‑language backbones can transfer the agent’s behavioral contract, enabling a 2B model to produce usable answers in 28.4% of cases on multimodal benchmarks. The approach uses parameter‑efficient LoRA adapters and synthetic step‑level preferences derived from GPT‑5 hard negatives to refine tool use and query quality. While synthetic preference learning and tool distillation provide incremental improvements, the main bottleneck identified is answer verification rather than search depth.

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• llms
• agents
• reinforcement-learning
• fine-tuning
• efficiency
• multimodal
• benchmarks