Diffusion language models (DLMs) can revise earlier predictions as context evolves, but rollback requires costly full-sequence recomputation. Conventional key-value (KV) caching assumes immutable historical states and is difficult to reconcile with such revision. We introduce Adaptive Reuse of Cached Hidden States for Efficient Rollback (Archer), a training-free caching method for rollback-capable DLMs. Archer recomputes the mutable response at every update while reusing prompt K/V within a bounded response-state neighborhood. This asymmetric boundary amortizes prompt computation without restricting rollback. Bounded reuse also delays feedback from tentative tokens while keeping response revision active. Our analysis characterizes the cache boundary, bounds state-dependent approximation error, and gives a decoder-margin condition for preserving full-refresh decisions. On code-generation benchmarks, Archer achieves the best mean performance of 33.63% with a 2.57x mean speedup, reaching 2.95x speedup on a single benchmark. On non-code benchmarks, Archer achieves up to 4.33x speedup over LLaDA-Confidence while improving average accuracy. Controlled analyses support the delayed prompt feedback mechanism. Our code is available at https://github.com/Hxnng/Archer

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