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Proposes IGRPO, a framework that adaptively allocates rollout budget based on informativeness of intermediate states for multi-turn LLM agents, unifying adaptive tree-structured exploration with policy learning. Experiments on seven search-augmented QA benchmarks show consistent improvements over baselines.