New Method Detects Distillation in Large Language Models Using Reference-Based Inference

Jul 14, 2026

A new arXiv preprint presents a method for detecting whether a large language model (LLM) was trained via distillation from another model, using reference-based membership inference. The approach compares a model and an earlier checkpoint from the same lineage to identify the likely teacher model, even when details of the distillation process are unknown. The method demonstrates near-perfect accuracy in both controlled experiments and real-world scenarios, and introduces statistical tests for teacher attribution and distillation detection. The study also provides new evidence of distillation relationships among models such as QwQ, DeepSeek-R1, and GPT-OSS.

Why it matters: This work provides a practical tool for detecting and attributing model distillation, addressing concerns about model provenance and compliance with usage policies.

Full story at: arXiv Machine Learning ↗