Sampling multiple chain-of-thought rationales from a language model can uncover correct solutions missed by a single attempt.
However, these rationales are typically lengthy, making repeated autoregressive generation costly.
Continuous latent reasoning offers a compact alternative, but point-valued methods condition repeated answer sampling on a single rationale, which can limit exploration of diverse paths and answer coverage.
We thus propose rationale distribution distillation (RDD), training a conditional flow over fixed-length latent sequences by distilling from multiple textual reasoning paths.
Furthermore, we expand the distillation targets through within-query mixup, interpolating between complete latent rationales for the same query.
The flow nonautoregressively generates the entire latent sequence, and a frozen language model autoregressively decodes answers from sampled rationale contexts.
Empirically, the resulting distributional reasoner achieves higher answer coverage than the evaluated point-valued latent reasoning baselines, particularly on advanced mathematical tasks.