What Transfers from Training-Only Priors? Separating Knowledge, Ordinal Targets, and Optimization

Abstract

Training-only teacher access lets a compact student deploy with local features and exported weights. This established distillation interface does not identify what transfers or when it helps. We separate label, query, capacity, and source-access budgets on three public credit archives and a previously unexamined bankruptcy archive. Shared tree parameters, shuffles, and optimizer interventions expose limitations of historical Ranking Prior Alignment; no tested single repair consistently rescues it. Heldout source queries and matched warmup and mappings retain a rank-listwise advantage over mapped staged KD in 24 credit configuration means and all six teacher--capacity means of a frozen new-archive test, with explicit query failures and stronger source-weight delivery under a different interface. A pretrained TabICLv2 follow-up at 100 labels initially improves matched supervision in only one of eight means; six of nine source-predicted policy gains reverse on target. A registered seen-archive intervention adds 500 or 2,000 unlabeled queries: all eight matched AUC means become positive under selected and fixed alpha. However, outer effects are mixed and no conditional paired-draw interval for these supervised contrasts lies wholly above zero. Increasing teacher context at 500 student labels does not beat matched supervision in any of eight ordinary-context means, despite stronger direct teacher performance. Rank KD retains substantial probability errors. Independent reloads verify zero guarded external inference accesses. Results support query/context boundary diagnostics, without universal superiority, population significance, a prospective transfer rule, or an LLM-specific advantage.