We study predictive properties of a reflective variant of Solomonoff induction formulated using reflective oracles (Fallenstein et al. 2015, Wyeth et al. 2025). We show that the resulting optimal predictor succeeds against computable predictive strategies that specify only part of the data-generating process. The argument relies on the fact that hypotheses computable from the predictor's own conditional probabilities remain within the reflective hypothesis class. In particular, the predictor learns incomplete models specified by computable interval restrictions on next-bit probabilities. We also establish weighted forms of unbiasedness and calibration. Finally, we discuss whether an optimal predictor's conditional probabilities suffice to compute every hypothesis in the reflective class.

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