Group-based reinforcement learning typically relies on coarse outcome-level signals, such as group accuracy or variance, for data selection or replay. Yet in multi-turn tool-use rollout groups, these signals miss structural differences between successful and failed trajectories: a failure may diverge into an unrelated tool chain or differ from a successful trajectory only at a local decision. We therefore introduce Group Structural Proximity (GSP), which measures failed-to-successful rollout alignment within a group using tool dependency graphs induced by tool calls and argument flow. A controlled diagnostic study with outcome-statistics-matched subsets further shows that GSP captures distinct learning dynamics: high-GSP data drives localized policy refinement, while low-GSP data induces broader policy movement and higher-entropy exploration. This positions GSP as an effective metric on the exploration–exploitation tradeoff. Building on this insight, we introduce Proximity-Guided Policy Optimization (PGPO), which considers replay buffers with high- and low- GSP under an Exploration-to-Refinement Schedule (ERS) for group-based training. On 𝜏²-Bench and ACEBench Agent, PGPO improves sample efficiency and downstream tool-use performance, demonstrating the benefit of structure-aware group comparison in multi-turn tool-use reinforcement learning.