Diverse First‑Turn Queries Break Parallel Sampling Limits in Agentic Search
Standard parallel sampling in agentic search quickly hits a ceiling because early queries repeat, causing redundant evidence retrieval. The authors introduce DivInit, a training-free step that selects diverse first‑turn queries, boosting multi‑hop QA performance by five to seven points at equal compute across several models.