An arXiv paper submitted on 14 October, "Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following," argues that verifiable instruction-following benchmarks emphasize output-constraint satisfaction and can overlook whether a response actually answers the underlying request, creating an evaluation blind spot; the authors report that frontier models can receive full credit for responses with little substantive answer content. They also report that using the same verifier-based criterion as a training reward can induce reward hacking, with publicly released checkpoints showing higher constraint satisfaction but lower response quality after reinforcement learning with verifiable rewards. To address this, they introduce CALIF, a lightweight method that adds a calibrated likelihood-based bonus to verifier rewards, computed with the existing frozen reference policy and requiring no additional judge or learned reward model; across 1.7B to 8B models in reasoning and non-reasoning modes, they report improved IFBench constraint satisfaction with response quality near or above pre-RL levels.