Reinforcement learning (RL) for multi-turn deep research agents suffers from a granularity mismatch: token-level methods incur high variance over long reasoning horizons, while sequence-level methods ignore the internal decision structure of multi-turn interactions. We introduce Block-wise Geometric Policy Optimization (BGPO), which treats each assistant turn—packaging reasoning and tool invocation into one semantically coherent unit—as an optimization-level macro-action and aggregates block-averaged log-importance ratios with equal weights across blocks. We prove that block-equal weighting minimizes a covariance-robust upper bound on ratio variance under weak inter-block coupling, with the approximation gap relative to sequence-length weighting bounded by block-length imbalance. To improve exploration efficiency, we expose human-defined rewrite operators—including decomposition, keyword extraction, synonym replacement, and HyDE—as a prompt-level exploration prior during RL rollouts. On seven knowledge-intensive QA benchmarks, our 7B system achieves an average judge score of 0.715 ± 0.010 over three training seeds, compared with 0.664 for Search-R1-7B. A controlled fixed-prior attribution study further confirms that block-level optimization independently improves answer quality, achieving 0.527/0.395 F1/EM compared with 0.482/0.350 for GRPO. Our code is publicly available at https://github.com/meiotoufa/Learning-to-Think-in-Blocks.