Flux-OPD: On-Policy Distillation with Evolving Contexts
Large language model training in open-ended domains lacks verifiable rewards, making task preferences difficult to formalize as effective supervision. Contexts derived from task feedback can provide richer supervision, yet fixed contexts cannot adapt as the student evolves, motivating contexts that evolve with student performance. However, directly using evolving contexts as in-training supervision results in unstable distillation targets and conflicting distributions, requiring mechanisms to stabilize the target and downweight conflicts. In this paper, we characterize context distillation through a reverse-KL decomposition, showing that the student is distilled toward the geometric mean ...
| Authors | Yuran Wang, Zekun Wang, Bohan Zeng et al. |
|---|---|
| Primary category | cs.LG |
| Published | 30 July 2026 |
| Last updated at source | 12 October 2026 |
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