Title: Reflective Decoding: Unsupervised Paraphrasing and Abductive Reasoning. Abstract: Pretrained Language Models (LMs) generate text with remarkable quality, novelty, and coherence while semantically conditioning on context. Yet applying LMs to the seemingly simpler problems of paraphrasing and infilling currently requires supervision, since these tasks break the left-to-right generation setup of pretrained LMs. We present Reflective Decoding, a novel unsupervised approach to apply the capabilities of pretrained LMs to non-sequential tasks. Our approach is general and applicable to two distant tasks -- paraphrasing and abductive reasoning. It requires no supervision or parallel corpora, only two pretrained language models: forward and backward. Reflective Decoding operates in two intuitive steps. In the contextualization step, we use LMs to generate many left and right contexts which collectively capture the meaning of the input sentence. Then, in the reflection step we decode in the semantic neighborhood of the input, conditioning on an ensemble of generated contexts with the reverse direction LM. We reflect through the generated contexts, effectively using them as an intermediate meaning representation to generate conditional output. Empirical results demonstrate that Reflective Decoding outperforms strong unsupervised baselines on both paraphrasing and abductive text infilling, significantly narrowing the gap between unsupervised and supervised methods. Reflective Decoding introduces the concept of using generated contexts to represent meaning, opening up new possibilities for unsupervised conditional text generation. 