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About this paper
Leyang Shen, Yaxin Gong, Yang Zhang, Chenxiao Fan, Jiakai Wang, Dong Wang, Yang Liu, Fuli Feng and 2 more
The authors introduce an external supervisor that monitors search-agent execution and intervenes when needed to limit goal drift. It separates a fast intervention-priority decision from a slower action-generation step, training both while keeping the executor frozen. Across seven in-domain retrieval-augmented question-answering benchmarks, RL steering raises mean token-F1 from 42.70 to 49.36, but evidence is limited to information-seeking tasks.
Summary by alphaXiv.
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