David Li & Alex Triplett

In this session, You.com’s engineering team will share how we post-trained Nemotron 3.5 Lightning, an open-weight 30B model, to become a stronger agent for web search and browsing, using You.com’s real-time web search API, NeMo Gym, supervised fine-tuning, and reinforcement learning. We’ll walk through the technical approach that enabled the team to iterate rapidly. It lifted accuracy on BrowseComp, a benchmark for agentic web search and browsing, from 37% to 45%, while making the agent more efficient with fewer tool calls. We will cover topics ranging from dataset preparation, supervised fine-tuning, and reinforcement learning to the agent harness and search infrastructure that made fast iteration possible. We’ll end with a blueprint for post-training and improving LLMs on real-world search tasks as well as a look at what’s next. This work was done in collaboration with NVIDIA and CoreWeave.