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This paper introduces FAER, an auditable full-trajectory replay framework for language model post-training that formalizes the gap between cache-level selection feedback and downstream learner utility, showing that a learner-aware selector (FAER-UTILITY) outperforms format-feedback and uniform baselines on GSM8K with Qwen2.5-1.5B-Instruct.