Deleting Bad Training Data Beats Architecture Tweaks, Says Engineer
generativist · x · 2026-07-24
An engineer shared a hands-on model training insight: deleting a massive amount of problematic training data yielded a significant performance lift without changing the model architecture. He emphasized that sexy architectural experiments are often distractions, while data quality remains the fundamental key.
More from Models
- Open-weight models are winning by running on hardware people can actually buy — max_paperclips · 2026-07-24
- Fable 5 appears to be tying an API into Slack for workflow automation — beechinour · 2026-07-24
- Leaks and early tests suggest Claude Opus 5 may be close to launch — 量子位 · 2026-07-24
- An overnight test compares Laguna-S-2.1 with Qwen 3.6 35B A3B — QuixiAI · 2026-07-24
- A “proper” unreadable tweet now needs both in-group decoding and an Opus refusal — generativist · 2026-07-24
- Anthropic and OpenAI both upgrade voice mode, but in opposite directions — 新智元 · 2026-07-24