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This paper studies the optimization layer for reinforcement learning with verifiable rewards (RLVR), proposing Isospectral Optimization (ISO) — a fixed-spectrum framework that reuses base model weight spectra while optimizing input/output singular frames. ISO-Merger and ISO-AdamW achieve strong performance with fewer training steps on reasoning and coding tasks.