OpenSeeker-v2-30B-SFT

OpenSeeker-v2-30B-SFT is a 30B-scale search agent trained with supervised fine-tuning (SFT) on informative and high-difficulty search trajectories. It is released as part of OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories.

• Hugging Face Paper: PolarSeeker/OpenSeeker-v2-paper
• GitHub: PolarSeeker/OpenSeeker

Highlights

Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by industrial giants. The typical industry recipe involves a highly resource-intensive pipeline spanning pre-training, continual pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL).

In this report, we show that when fueled with informative and high-difficulty trajectories, a simple SFT approach can be surprisingly powerful for training frontier search agents. By introducing three simple data synthesis modifications, we establish a stronger baseline:

• Scaling knowledge graph size for richer exploration.
• Expanding the tool set size for broader functionality.
• Applying strict low-step filtering to keep high-difficulty trajectories.

Trained on merely 10.6k data points, OpenSeeker-v2 achieves state-of-the-art performance across four benchmarks among 30B-sized agents with the ReAct paradigm:

• 46.0% on BrowseComp.
• 58.1% on BrowseComp-ZH.
• 34.6% on Humanity's Last Exam.
• 78.0% on xbench.

OpenSeeker-v2 surpasses Tongyi DeepResearch, which is trained with a heavier CPT+SFT+RL pipeline and achieves 43.4%, 46.7%, 32.9%, and 75.0% on the same benchmarks, respectively.

Notably, OpenSeeker-v2 represents the first state-of-the-art search agent within its model scale and paradigm to be developed by a purely academic team using only SFT. We are excited to open-source the OpenSeeker-v2 model weights and share our simple yet effective findings to make frontier search agent research more accessible to the community.