Serial subtitle translation requires work-specific terminology and translation conventions to be maintained across episodes, despite scarce bilingual supervision. Previously finalized subtitles provide both adaptation data and translation memory, but our experiments show that the benefit of combining supervised adaptation and retrieval varies across series. We introduce SerialDistill, which combines LoRA-based domain adaptation with retrieval-conditioned on-policy distributional distillation (OPD). A Qwen3-8B student generates translation trajectories, while a frozen Qwen3-14B teacher provides token-level supervision under the same bilingual context retrieved exclusively from previously finalized episodes. On Japanese-to-Chinese translation across two anime series, SerialDistill improves over the non-distilled LoRA-8B+RAG baseline by up to 3.80 chrF and 4.15 BLEU, measured on pooled held-out episodes. The student also outperforms the larger retrieval-augmented teacher, which has not undergone domain adaptation. These results support retrieval-conditioned distillation as an additional adaptation stage for data-scarce serial subtitle translation.