Sangmin Lee

The work identifies a teacher-training failure mode in which standard flow-based teachers pair noise with replay targets independently, routing nearby noise samples toward conflicting coordination modes and producing samples between valid modes that the distillation loss propagates to students; it proposes MoSDOT, which summarizes multimodal replay into a finite mode support with prescribed capacities and uses conditional semi-discrete optimal transport to assign each noise sample to a single mode before teacher training, improving endpoint quality and routing consistency on controlled diagnostics and offline MARL benchmarks, and studies a shared-randomness variant to expose the residual gap intrinsic to strict-product execution.

The work identifies a teacher-training failure mode in which standard flow-based teachers pair noise with replay targets independently, routing nearby noise samples toward conflicting coordination modes and producing samples between valid modes that the distillation loss propagates to students; it proposes MoSDOT, which summarizes multimodal replay into a finite mode support with prescribed capacities and uses conditional semi-discrete optimal transport to assign each noise sample to a single mode before teacher training, improving endpoint quality and routing consistency on controlled diagnostics and offline MARL benchmarks, and studies a shared-randomness variant to expose the residual gap intrinsic to strict-product execution.