Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning

Abstract: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.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2610.19871 [cs.CL]

(or arXiv:2610.19871v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.19871 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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