Autolearn: Learn by Surprise, Commit by Proof
We propose Autolearn, a framework that enables language models to learn from documents they read, with no external supervision. Passages that produce anomalously high per-token loss are flagged, verified through a self-generated Q&A chain, and trained on with conviction-proportional $β_2$ adjustment. We introduce the perturbation gap (paraphrase-to-original perplexity ratio) as a metric that distinguishes memorization from understanding. The key mechanism is the training data format: Q&A-format training drives the perturbation gap below the pre-trained baseline (2.098 vs. 2.204, $Δ= -0.106$, $> 10σ$), suppressing token-sequence memorization, while standard fine-tuning's best attempt remai...
| Authors | Kang-Sin Choi |
|---|---|
| Primary category | cs.LG |
| Published | 2 April 2026 |
| Last updated at source | 7 May 2026 |
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