Researchers trained a Deep Research agent with 32 H100s and open-sourced everything
Summary
Researchers trained a Deep Research agent using 32 H100 GPUs and open-sourced all components, enabling community access and further development.
Similar Articles
@Ex0byt: A must bookmark.. tiny cracked team, 4 H100 nodes, open source 3 stage recipe, trained on 8k synthetic rubric tasks, fu…
A small team trained a frontier-level Deep Research Agent on an academic budget using only 32 H100s and 8K synthetic samples, releasing fully open weights, code, and paper for models from 2B to 35B that match or beat closed frontier agents on key benchmarks.
@KaiZhang_CS: Check out one of the best open-source search agents trained by @jianxie_ !! glad to see early experience methods work o…
Yu Su's team trained a frontier Deep Research Agent on an academic budget using 8K synthetic samples and RL, releasing fully open training infrastructure and models from 2B to 35B parameters.
Deep research System Card
OpenAI launches Deep Research, an agentic capability powered by an early version of o3 that conducts multi-step internet research for complex tasks, with comprehensive safety testing and privacy protections implemented before rollout to Pro users.
S1-DeepResearch: Beyond Search, Toward Real-World Long-Horizon Research Agents
This paper introduces S1-DeepResearch-32B, an open-source model and 15K trajectory dataset for deep research agents, achieving state-of-the-art performance across 20 benchmarks by jointly modeling information acquisition, knowledge synthesis, and planning.
@VukRosic99: A DeepSeek researcher just open-sourced his AutoResearch personal project. For the first time, the AutoResearch Agent a…
A DeepSeek researcher open-sourced AutoResearch, an autonomous framework that can plan, execute, and debug RL experiments on the DeepSeek 285B model without human intervention, accompanied by a self-play survey paper.