Reinforcement learning (RL) for long-horizon agents faces two key challenges. First, parallel linear rollouts tend to converge toward homogeneous reasoning patterns, leading to cognitive-mode collapse. Second, existing methods refine credit assignment along only a single dimension, making it difficult to simultaneously achieve state-aligned comparison and meta-reasoning discrimination. We propose STARK (**S**trategic and **T**actical **A**dvantage **R**einforcement via Confidence Guided **K**eypoint Exploration) to resolve both issues. STARK performs corrective exploration through confidence-driven branching at uncertain decision points and injects reflection into new branches. To empower these keypoint-expanded trajectories, STARK introduces a dual-track strategic and tactical advantage estimator: The strategic track compares reasoning modes under shared environmental observations, while the tactical track evaluates execution quality within each meta-reasoning tag group. Extensive experiments on ALFWorld, ScienceWorld, and WebShop show that STARK consistently outperforms strong baselines across model scales and task domains, and enables a 1.5B model to surpass GPT-5 on the out-of-domain settings.