OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents
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| Authors | Shuang Chen et al. |
| Year | 2026 |
| HF Upvotes | 92 |
| arXiv | 2605.05185 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Deep search has become a crucial capability for frontier multimodal agents, enabling models to solve complex questions through active search, evidence verification, and multi-step reasoning. Despite rapid progress, top-tier multimodal search agents remain difficult to reproduce, largely due to the absence of open high-quality training data, transparent trajectory synthesis pipelines, or detailed training recipes. To this end, we introduce OpenSearch-VL, a fully open-source recipe for training frontier multimodal deep search agents with agentic reinforcement learning. First, we curated a dedicated pipeline to construct high-quality training data through Wikipedia path sampling, fuzzy entity rewriting, and source-anchor visual grounding, which jointly reduce shortcuts and one-step retrieval collapse. Based on this pipeline, we curate two training datasets, SearchVL-SFT-36k for SFT and SearchVL-RL-8k for RL. Besides, we design a diverse tool environment that unifies text search, image search, OCR, cropping, sharpening, super-resolution, and perspective correction, enabling agents to combine active perception with external knowledge acquisition. Finally, we propose a multi-turn fatal-aware GRPO training algorithm that handles cascading tool failures by masking post-failure tokens while preserving useful pre-failure reasoning through one-sided advantage clamping. Built on this recipe, OpenSearch-VL delivers substantial performance gains, with over 10-point average improvements across seven benchmarks, and achieves results comparable to proprietary commercial models on several tasks. We will release all data, code, and models to support open research on multimodal deep search agents.
Engineering Breakdown
Plain English
OpenSearch-VL is an open-source framework for training multimodal search agents that can solve complex questions through active search and multi-step reasoning. The paper addresses a critical gap: while frontier models exist, they're hard to reproduce because their training data, synthesis pipelines, and recipes are proprietary. The authors release a complete pipeline including data curation (Wikipedia path sampling, entity rewriting, visual grounding) and training methodology using agentic RL.
Key Engineering Insight
The core innovation is a systematic data pipeline that prevents the model from taking shortcuts—fuzzy entity rewriting and source-anchor visual grounding force the agent to actually search and reason rather than memorizing single-step retrieval patterns. This is the difference between models that look like they search and ones that genuinely perform multi-step reasoning.
Why It Matters for Engineers
If you're building a production search agent or RAG system, this gives you a reproducible recipe instead of trial-and-error. The open pipeline means you can adapt their data construction methods to your domain without waiting for proprietary model releases. Agentic RL training unlocks better generalization than supervised fine-tuning alone, which directly impacts real-world search quality and user-facing reliability.
Research Context
Multimodal search has been limited by closed-source training recipes—companies keep their best agents proprietary. This paper breaks that by showing how to systematically construct training data that prevents shortcut learning and enables genuine multi-step reasoning. It advances the field from 'black box frontier models' to 'reproducible open recipes,' which matters because it democratizes access to agent training techniques and allows the community to build on proven methods rather than reinventing from scratch.
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