AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories
LLM-based agents are increasingly deployed in real-world applications through tool-use APIs, yet training them for specific environments remains fundamentally difficult: real-world applications provide no pre-defined tasks or verifiers, no faithful simulators, and limited budget for large-scale environment interaction. In this paper, we propose \textbf{AgentBrew}, an offline training framework that learns effective tool-use policies from a single batch of raw interaction trajectories, without task verifiers or iterative on-policy rollouts. The agent first explores the target environment to collect a raw trajectory corpus without quality filtering. To extract training signal from this nois...
| Authors | Zhiyi Lyu, Yewen Li, Longtao Zheng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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