Phantom Gains: Auditing Self-Improvement Against a Measured Null
Score
78揭示了自训练评估中多种被忽略的测量伪影，并提供了可复用的审计方法，信息增量显著。
实验设计严谨，通过冻结对照和 FDR 控制保证了结论的稳健性，分析深入。
提出的审计方法和零假设构造可直接用于多臂 LLM 评估，帮助实践者避免虚假结论。
紧贴当前 LLM 自我改进与评估方法热点，及时回应社区对可复现性的担忧。
来自 arXiv 预印本，尚未经过正式同行评审，但方法透明、数据详实。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper uncovers multiple overlooked measurement failures and provides empirical comparisons with rigorous statistical control, offering high methodological value.
AI commentary
「This paper is essential for teams working on model self-training or comparative evaluation. It reminds us that without proper controls, many 'capability gains' may be phantom artifacts from inference settings or statistical noise. I recommend adopting its null-audit approach as a standard check in evaluation pipelines.」
Quick overview
Auditing self-improvement in language models requires distinguishing real gains from measurement artifacts. This paper examines three rounds of rank-32 LoRA self-training on Qwen3-8B against a frozen control through an identical pipeline, identifying seven measurement failures that invert reported findings when controls are absent. A single greedy-decode ledger fabricates capability changes on an untrained model due to inference batching; the expansion statistic assigns that model a rate of 0.280. The authors propose a per-problem exact test under false-discovery-rate control, which finds nothing on held-out replicates and remains stable under multiple-testing rules. Applied to a matched ladder of arms, external distillation improves problems rarely reached by the base model, while three self-training forms do not; a regression rejects the asymmetry as a byproduct of distillation's larger overall gain (p<10^-8). Self-training corrupts baseline-solved problems at high rates. The study concludes that transition-level auditing demands a separately measured null for every statistic, built from baseline replicates already owned by multi-arm studies.
- Proposes auditing self-training evaluation with frozen controls, identifying seven measurement failures, several of which are standard practice.
- Shows that single greedy decoding and inference batching can fabricate capability gains on an untrained model, with an expansion statistic misreporting a rate of 0.280.
- Introduces a per-problem exact test under FDR control that detects nothing on held-out replicates and is robust across multiple-testing rules.
- Finds external distillation outperforms three self-training forms, while self-training corrupts baseline-solved problems at high rates.
Full text
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