Scaling Interactive Learning Resources for LLM Agents: A Survey of Environment and Task Synthesis
Large language model (LLM) agents can learn through interaction with external environments, obtain training signals and reusable experience from task execution. Scaling this form of learning requires a sufficient supply of interactive environments and tasks grounded in those environments, with explicit objectives and c...
Yingchen Zhang, Changjiang Zhou, Ruqing Zhang et al.
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