Instruction-following information retrieval (IF-IR) requires retrievers to satisfy both topical intent and instance-specific relevance constraints. We propose dual-view training, a data synthesis strategy based on polarity reversal. Given a query, an instruction-relevant document, and a topically related document excluded by that instruction, an LLM generates a complementary instruction under which the two documents exchange relevance labels. The resulting paired views directly penalize instruction-agnostic representations. At a matched 880k-example budget, replacing the non-instruction half of a Promptriever-style joint-data recipe with polarity-reversed examples improves FollowIR p-MRR from 5.27 to 8.30 (+57.5%) on a 305M-parameter encoder while increasing its instruction-conditioned Score from 20.85 to 21.38. The relative p-MRR gain repeats on a 560M-parameter backbone. The proposed recipe also improves InfoSearch nDCG@5 and shows no average degradation on four evaluated BEIR datasets with plain queries. A size-matched paraphrase control does not reproduce the instruction-sensitivity gains, supporting the conclusion that label reversal, rather than additional LLM-generated wording, provides the key supervision signal.