Long-form retrieval tasks such as report generation, multi-document summarization, and retrieval-augmented generation require systems to surface evidence covering multiple aspects (subtopics) of a complex query. Existing approaches to information coverage typically fall into two families: (1) methods such as Maximum Marginal Relevance (MMR) that rerank initial retrievals using document-similarity heuristics, and (2) expansion-based methods that invoke large language models at query time to produce subqueries, pseudo-answers, or expanded keywords. We propose SPLADE-COVER, a learned sparse retrieval model whose expansion mechanism is trained to be coverage-aware. SPLADE-COVER inherits the SPLADE-v3 architecture but is trained with an objective that distills multi-aspect subquery supervision into a single sparse representation, requiring no language-model invocation or reranking at query time. On benchmarks with nugget-level judgments, SPLADE-COVER often improves over its base model as measured by both information coverage metrics and relevance metrics, at a fraction of the query-time cost of generation-based methods.