# OpenSeeker-v2: A Cutting-Edge Search Agent Trained with Only 10.6k Data Points

> This article introduces OpenSeeker-v2, a cutting-edge search agent fully developed by an academic team and trained solely via supervised fine-tuning (SFT). It outperforms industrial-grade models using complex CPT+SFT+RL processes in four authoritative benchmark tests.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-05T17:55:25.000Z
- 最近活动: 2026-05-06T03:18:23.253Z
- 热度: 141.6
- 关键词: 搜索智能体, 大语言模型, 监督微调, 数据合成, BrowseComp, ReAct, 知识图谱, 工具学习
- 页面链接: https://www.zingnex.cn/en/forum/thread/openseeker-v2-10-6k
- Canonical: https://www.zingnex.cn/forum/thread/openseeker-v2-10-6k
- Markdown 来源: floors_fallback

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## OpenSeeker-v2 Introduction: Academic Team Trains a Cutting-Edge Search Agent with 10.6k Data Points via SFT

This article introduces the OpenSeeker-v2 search agent developed by an academic team. Trained solely through supervised fine-tuning (SFT) using 10.6k data points, it outperforms industrial-grade models adopting complex CPT+SFT+RL processes in four authoritative benchmark tests.

## The Dilemma of Industrial Monopoly in the Search Agent Field

Deep search capability is the core competitiveness of cutting-edge LLM agents but has long been monopolized by tech giants. The mainstream industrial approach uses a resource-intensive four-stage process: pre-training → continuous pre-training (CPT) → SFT → reinforcement learning (RL), requiring thousands of GPU hours and millions of dollars in investment—something academic teams can hardly afford.

## OpenSeeker-v2's Breakthrough Approach and Data Synthesis Strategies

The academic team broke through via high-quality training trajectories + SFT method, with three key data synthesis strategies: 1. Knowledge graph scale expansion to increase information depth and breadth, approaching real complex scenarios; 2. Toolset scale expansion to enhance tool usage flexibility and collaboration capabilities; 3. Strict low-step filtering to retain training trajectories with high information density and strong reasoning efficiency.

## OpenSeeker-v2's Performance: Outperforming Industrial-Grade Models

Using a 30B model and the ReAct paradigm, OpenSeeker-v2 performed excellently in four benchmarks: BrowseComp English 46.0% (Tongyi DeepResearch 43.4%), Chinese 58.1% (Tongyi 46.7%); Humanity's Last Exam 34.6% (Tongyi 32.9%); xbench 78.0% (Tongyi 75.0%).

## Technical Significance and Insights of OpenSeeker-v2

1. Data quality over quantity: 10.6k high-quality samples outperform massive unfiltered data; 2. Simple methods can beat complex processes: SFT combined with high-quality data outperforms complex industrial processes; 3. Academic democratization: The first SOTA search agent developed by an academic team, with open-source weights lowering the research entry barrier.

## Limitations and Future Directions of OpenSeeker-v2

There is room for improvement: 1. Scale limitation—explore larger models; 2. Expand multimodal capabilities (images, videos); 3. Improve real-time information acquisition efficiency; 4. Enhance safety and controllability.

## Conclusion: A Milestone in AI Research Democratization

OpenSeeker-v2 proves that academic teams can compensate for resource shortages with ingenuity, marking an important milestone in AI research democratization. Open-source model weights will推动 the development of search agent technology toward an open and inclusive direction.