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cs.AI2026
AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories
Zhiyi Lyu, Yewen Li, Longtao Zheng +7
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…
cs.AI2026
SAPO: Single-Rollout Autoregressive Policy Optimization for Agentic Reinforcement Learning
Dayang Liang, Lang Feng, Bo An +1
Agentic reinforcement learning (RL) has emerged as an important post-training approach for enhancing the capabilities of Large Language Models (LLMs). However, existing methods fac…