Hugging Face Trending Papers
Advancement or Acceleration? How Data Curation Improves LLM Training
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
3d ago
Advancement or Acceleration? How Data Curation Improves LLM Training
arXiv:2610. 36983v1 Announce Type: cross Abstract: While data curation is widely credited with improving large language model training, a basic question remains unresolved: does curation expand what a model can ultimately learn, or merely reach the same solution faster?
arXiv Machine Learning
Sep 30
UnlearningSoup: Is Repeated Tuning Necessary for Large Language Model Unlearning?
arXiv:2609.37076v1 Announce Type: new Abstract: Large language models trained on vast corpora inherently risk memorizing harmful content that may later re-emerge in their outputs. To mitigate this is...
Hugging Face Trending Papers
5d ago
CaliMem: Search-Efficient Data Agent via Calibrated Memory
Data curation is a first-order design choice in machine learning, yet tailoring training data to a downstream model and task remains costly in labor, expertise, and compute. Datasets are therefore oft...
arXiv AI
3d ago
Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training
The paper introduces Dr. Post-Training, a framework that treats general training data as a data‑induced regularizer to prevent overfitting to scarce target objectives, rather than merely a pool for selection. It constructs a feasible set of model update directions from general data and projects target‑driven updates onto this set, unifying standard training and existing data‑selection methods as special cases along a bias‑variance spectrum. Experiments on SFT, RLHF, and RLVR demonstrate that these methods outperform both standard training and state‑of‑the‑art data‑selection baselines, especially when general and target data are highly misaligned, while system optimizations keep overhead minimal.
arXiv Computation and Language
Oct 16
Data Strategies for Large Language Models: A Unified Perspective on the Training Lifecycle
The paper reviews data strategies for large language models, emphasizing that data strategy—selecting, weighting, and scheduling training data—determines model capabilities within a fixed data budget. It treats high‑quality data as having training‑dependent utility, varying with the target objective, model, and training state. The authors organize existing methods by decision granularity, model dependence, and temporal adaptivity, comparing their computational costs and empirical support, and note limited evidence for transfer across model scales and end‑to‑end cost parity.
arXiv AI
4d ago
CaliMem: Search-Efficient Data Agent via Calibrated Memory
arXiv:2610.34500v1 Announce Type: new Abstract: Data curation is a first-order design choice in machine learning, yet tailoring training data to a downstream model and task remains costly in labor, e...