dailyai.report

The DivInit method improves breadth scaling in agentic search by selecting diverse seed queries from a single model call. Standard parallel sampling often produces redundant first-turn queries, leading to overlapping evidence retrieval. This training-free intervention forces trajectory divergence. It ensures more comprehensive information gathering for open-weight models during test-time scaling.