One of the cornerstones of the distributed complexity theory is the derandomization result by Chang, Kopelowitz, and Pettie [FOCS 2016]: any randomized LOCAL algorithm that solves a locally checkable labeling problem (LCL) can be derandomized with at most exponential overhead. The original proof assumes that the number of random bits is bounded by some function of the input size. We give a new, simple proof that does not make any such assumptions-it holds even if the randomized algorithm uses infinitely many bits. While at it, we also broaden the scope of the result so that it is directly applicable far beyond LCL problems.

翻译：分布式复杂性理论的基石之一是Chang、Kopelowitz与Pettie在[FOCS 2016]中提出的去随机化结果：任何解决局部可检查标记问题（LCL）的随机化LOCAL算法，均可通过至多指数级开销实现去随机化。原证明假设随机比特数受限于输入规模的某个函数。我们提出一种全新的简洁证明，该证明不依赖此类假设——即使随机化算法使用无限比特流，结论依然成立。与此同时，我们还将该结果的适用范围大幅扩展，使之能够直接应用于远超出LCL问题的范畴。