Mathematical word problems with plausible irrelevant context expose a failure mode in which language models reason from facts that should not support the target solution. We formulate distractor-robust mathematical reasoning as relevance-boundary supervision: a model should separate Signal facts from Distractor facts before solving, and then ground the solution in Signal facts only. We introduce ReBound, a post-training framework that constructs paired clean–distractor examples, trains boundary-first traces with supervised fine-tuning, and applies correctness-guided reinforcement learning to discourage Distractor use. Across three open base LLMs and six mathematical reasoning benchmarks, ReBound consistently improves distractor robustness over CoT prompting and strong training-based baselines; on Qwen3-0.6B, the distractor benchmark average increases from 34.92 to 48.29. ReBound also preserves competitive clean and perturbation performance. Process diagnostics, ablations, and output-length analysis show that these gains are tied to better fact use and boundary behavior, rather than longer generations or answer-only optimization.The code is available at https://anonymous.4open.science/r/Rebound-5FB8