Eliciting high-quality reasoning trajectories to supervise Large Language Models (LLMs) in sequential recommendation is notoriously difficult. In massive item pools, standard data curation methods such as autonomous exploration or rejection sampling suffer from severe sparsity. LLMs’ context-utilization bias exacerbate this challenge by overusing noisy interaction histories, yielding diffuse and weakly grounded reasoning paths that frequently fail to retrieve the target item. To overcome this data curation bottleneck, we propose GEARec (Grounded Reasoning Curation for Anchor-based Recommendation), a framework based on Dual-Perspective Anchor Extraction and Hybrid Reasoning Generation. By combining the semantic strengths of LLMs with collaborative filtering (CF) signals, GEARec identifies a high-relevance anchor item from the user’s history to construct highly focused, reachability-aware reasoning trajectories for supervised fine-tuning. Experiments on multiple datasets demonstrate that GEARec effectively curates superior supervision signals, reducing context overuse and consistently outperforming strong baselines in recommendation accuracy.