How NVIDIA Uses Tavily to Post-Train Deep Research Agents

NVIDIA argues that specialized research models can reduce the high inference costs of agentic web research by learning efficient planning, retrieval, and synthesis behaviors through post-training. Using its AI-Q deep researcher powered by GPT-OSS-120B, NVIDIA generated roughly 80,000 research trajectories containing plans, Tavily web searches, retrieved evidence, and responses, filtered about 67,000 for quality, and used them to fine-tune Nemotron 3 Super. The resulting model served as the evidence-gathering researcher within an AI-Q system that separates planning, research, and orchestration, and NVIDIA reported first-place results on DeepResearch Bench I and II in March 2026. The account emphasizes that high-quality retrieval reduces wasted searches, irrelevant context, and failed training trajectories, while using the same search tool in training and deployment helps models learn behavior suited to their real operating environment.

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