An academic research team has introduced OpenSeeker-v2, a novel search agent that achieves state-of-the-art performance with a remarkably small dataset and straightforward supervised fine-tuning (SFT). This new agent leverages three key improvements in data synthesis: an expanded knowledge graph size, an enlarged tool set, and rigorous low-level filtering. These enhancements allowed OpenSeeker-v2 to surpass the performance of more complex, resource-intensive industry models, demonstrating a highly efficient approach to advanced AI development. Specifically, OpenSeeker-v2 recorded the highest performance among 30B-scale ReAct paradigm agents across four major benchmarks: BrowseComp, BrowseComp-ZH, Humanity's Last Exam, and xbench. Its results notably exceeded those of Tongyi DeepResearch, an industry model that utilizes a combination of continuous pre-training (CPT), SFT, and reinforcement learning (RL), despite OpenSeeker-v2 being trained on only 10.6 thousand data points. This achievement highlights the potential for significant advancements even with limited resources. The development of advanced large language model (LLM)-based search agents has traditionally been perceived as an exclusive domain for large industry corporations, demanding substantial investments in computational resources and complex training methodologies. Conventional approaches often involve extensive pre-training, continuous pre-training, supervised fine-tuning, and reinforcement learning, all of which require significant capital and infrastructure. The academic team's success with OpenSeeker-v2, achieved with a mere 10.6 thousand data points and simple SFT, marks a significant departure from this norm. This accomplishment is particularly noteworthy as it demonstrates that cutting-edge performance can be attained without the massive resource outlays typically associated with such projects, thereby lowering the barrier to entry for research and development in this critical area and fostering greater accessibility. This research suggests a potential paradigm shift in the development of search agents, emphasizing that the quality of data and the efficiency of synthesis strategies can be as crucial as, if not more important than, the sheer quantity of data input. By showcasing a more resource-efficient pathway to state-of-the-art capabilities, OpenSeeker-v2 opens new avenues for innovation and democratizes access to advanced AI development. The findings are expected to stimulate more active participation from the open-source community, encouraging smaller teams and individual researchers to contribute to and refine search agent technologies. This could lead to a broader range of specialized and efficient search solutions, fostering a more diverse and competitive landscape in the AI industry and accelerating overall progress. Source: https://arxiv.org/abs/2605.04036v1