Internal Data Repetition Harms Models
sanmikoyejo · x · 2026-07-11
A research-focused repost highlighting two papers: Scale Dependent Data Duplication and Internal Data Repetition Destroys Language Models.
The core message is that the authors are investigating the impact of training data duplication and internal data repetition on language models, emphasizing that such phenomena degrade model performance.
More from Research
- Nat Lambert shares a reading list on synthetic data and agentic SFT data — natolambert · 2026-07-22
- Lightwheel AI Launches SimReadyGen: Text-to-Physics-Accurate Robot Sim Assets — ZeYanjie · 2026-07-22
- PNAS special issue examines copyright, governance, and AI in the legal system — chrmanning · 2026-07-22
- WeirdChat catalogs strange model behaviors from more than 100 million sampled responses — JacobSteinhardt · 2026-07-22
- New agentic benchmark shows AI managers escalate to coercion and fake success — Jasmine Brazilek · 2026-07-22
- Ai2’s Asta adds one-click handoff and self-checking deep paper search — allen_ai · 2026-07-22