Group Relative Policy Optimization (GRPO) trains Chain-of-Thought reasoning with verifiable rewards, but estimating thought-level advantages without value functions often suffers from high variance. Although tree-style branching is used in practice to reduce the variance, it lacks a theoretical explanation of why it works and whether it is important or even potentially necessary. We study thought-level advantage estimation in GRPO from a variance perspective under a minimal tree-style setting where multiple answers are sampled for each thought. Using the multivariate delta method, we reveal an asymmetry in how different sampling dimensions affect variance. Increasing the number of sampled thoughts ($K$) leaves a strictly positive variance floor, whereas increasing the number of answers per thought ($M$) induces a monotonic decrease in variance, asymptotically decreasing it to zero. This implies that accurate thought-level advantage estimation is impossible through scaling thought sampling alone, making branching a potentially necessary mechanism rather than a heuristic. Experiments further provide empirical evidence for both the effectiveness and necessity of answer-level branching, demonstrating improved optimization stability, training efficiency, and final performance not only in math but also across a broad range of vision domains and under different model architectures and sizes.