data-centric

Introduces Data-Centric Parallel (DCP), a method for training deep learning models on variable long sequences by dynamically adjusting runtime settings per batch, achieving up to 2.88x speedup on 32 H200 GPUs with only 10 lines of code integration.

This paper proposes DataRx, a missingness-aware sampling method that selects safety-critical examples to preserve LLM safety during task-specific fine-tuning, reducing attack success rates from 59.23% to 13.70% on Llama3-8B-Instruct.

This paper introduces DMAPO, a method for preference optimization that uses multi-evaluator consensus to select high-confidence on-policy responses, achieving strong alignment with significantly less data (only 3.45% acceptance rate) and outperforming baselines like SimPO on several benchmarks.

WeightsLab is an open-source, PyTorch-native tool that allows teams to pause training, inspect live loss signals, and catch data issues like mislabels and class imbalance before they affect model performance. It is designed for computer vision engineers working with images, videos, and LiDAR point clouds.

This paper proposes PreUnlearn, a framework for auditing collateral knowledge damage in LLM unlearning before execution, using data-centric analysis to predict downstream damage across semantic layers.

This paper introduces LIMMT, a data-centric study showing that training with high-quality, minimal subsets of motion data (under 3% of AMASS) outperforms using the full dataset for physics-based humanoid motion tracking, defining motion data quality through physics feasibility, diversity, and complexity.

This survey reframes the alignment tuning of large language models as a data pipeline design problem, decomposing it into three stages: response synthesis, preference evaluation, and preference instantiation. It identifies design trade-offs and failure modes, and outlines open challenges such as prompt-level alignment and agentic settings.

This paper challenges the belief that code improves reasoning in language models, finding through controlled pretraining experiments that code alone primarily enhances programming ability, while reasoning gains come from structured reasoning traces like code-text and math-text mixtures.

This paper presents EMA, a model adaptation system for learning-based systems that reduces training and labeling costs while improving system performance in evolving environments.

Investigates whether synthetic layered data can improve graphic design decomposition, finding that synthetic data outperforms non-scalable datasets and enables balanced layer-count distributions.

DataFlex is a unified framework for data-centric dynamic training of LLMs that integrates sample selection, domain mixture adjustment, and sample reweighting with compatibility for standard workflows and efficient large-scale deployment, achieving consistent improvements over static training.