OpenResearcher: a reproducible and scalable pipeline for training deep research agents

OpenResearcher, an EMNLP 2026 project by researchers from several universities and Lambda, addresses the challenge of training deep research agents by generating reproducible synthetic research trajectories rather than relying on expensive human demonstrations or unstable live-web data. It creates an offline environment containing 15 million documents and search, open, and find tools, where GPT-OSS-120B generated more than 97,000 long-horizon trajectories involving planning, retrieval, evidence gathering, failed tool calls, and iterative reasoning. These trajectories were filtered and used to fine-tune the smaller NVIDIA Nemotron-3-Nano-30B-A3B model with long-context training, producing an agent that achieved 54.8% on BrowseComp-Plus and substantially outperformed several larger proprietary and open-source systems on reported benchmarks. Although trained entirely offline, the model also generalized to live-web-oriented evaluations, supporting the claim that large-scale synthetic research experience can transfer to real-world research tasks. The project illustrates a shift toward allocating substantial compute to synthetic-data generation, with teacher inference using 64 H100 GPUs for two days compared with eight H100 GPUs for eight hours of student post-training, and its trajectories have since been adopted in NVIDIA research and synthetic-data tooling.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.