FLUX: Data Worth Training On
Modern large language model training is no longer limited by data availability, but by the inability of existing preprocessing pipelines to simultaneously achieve massive scale and high data quality. Current approaches are forced to sacrifice one for the other: either aggressively filtering to improve quality at the cost of severe token loss, or retaining large volumes of data while introducing substantial noise. In this work, we introduce FLUX, a preprocessing pipeline specifically designed to break this long-standing trade-off by maximizing token retention while enforcing rigorous quality control. Models trained on FLUX-curated data consistently outperform prior methods. A 3B-parameter ...
| Authors | Gowtham, Sai Rupesh, Sanjay Kumar et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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