Data-centric LLM training with dynamic sample selection, domain mixture optimization, and example reweighting inside the LLaMA-Factory training loop.
DataFlex is a data-centric training framework that enhances model performance by either selecting the most influential samples, optimizing their weights, or adjusting their mixing ratios.
A Data-Centric RL and On-Policy Distillation Framework for LLM Post-Training
(ACL 2026 Main) LLMSurgeon recovers the pretraining data mixture of any LLM from only its generated text — no weights, no training data. A calibrated domain classifier plus label-shift correction de-blurs biased predictions. Ships with LLMScan, a benchmark on 8 open-source LLMs.
Unofficial reproduction of Nemotron-CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training (NeurIPS 2025, arXiv:2504.13161) — search-found mixtures beat uniform baselines +0.014–0.031 STEM at d28; novel finding: selection-mechanism winner's curse; Ascend NPU backend.
Exact token-weighted cluster totals and reproducible calibration for NVIDIA Nemotron-ClimbMix
To associate your repository with the data-mixture topic, visit your repo's landing page and select "manage topics."