Supervised fine-tuning (SFT) is a core stage in post-training general-purpose language models, but modern SFT training corpora are often large, heterogeneous mixtures, making data selection important under fixed token budgets. Simple ranking approaches can be informative yet insufficient: ranking alone does not determine how to allocate a fixed token budget across examples. We study the role-specific use of likelihood-derived signals for token-budgeted subset construction. An external instruction-tuned reference model computes response-side negative log-likelihood scores to rank training examples. We then combine this ranking with base model likelihood filtering to restrict the candidate region and response-side cosine filtering to reduce redundancy in the selected subset. In the OLMo fine-tuning setting studied here, ranking examples only by reference-model likelihood underperforms the random baseline, whereas the same ranking becomes more effective when combined with base model likelihood filtering and cosine-based redundancy control. Our results support viewing token-budgeted SFT data selection as a budget-allocation problem rather than only as an example-ranking problem.