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Adaptive Sampling

topic3 events
papersTODAY 04:00 UTC

Adaptive Language Sampling Method Targets Cross-Lingual Transfer for Low-Resource Languages

A new arXiv paper proposes an online adaptive sampling strategy for realigning multilingual language models, aiming to improve cross-lingual transfer to extremely low-resource languages. The authors note that existing realignment approaches typically use uniform or random sampling, which may underuse informative language pairs. Their method adjusts sampling dynamically as training proceeds within a distributed setup.

multilingual/low-resource/training/arxiv
papersTODAY 04:00 UTC

Attention-Discounted Adaptive Sampler Proposed for Masked Diffusion Language Models

A new arXiv paper introduces an adaptive sampling method for masked diffusion language models that decides which tokens to commit during each denoising step. The approach targets a known failure mode where individually confident positions become unsafe when decoded in parallel, aiming to preserve accuracy while still reducing the number of inference iterations. The work is a revision of an earlier preprint and has not been peer reviewed.

diffusion-models/inference/sampling/research
papersSEP 10 04:00 UTC

Gradient-guided Gaussian adaptive sampling proposed for training physics-informed neural networks

A new arXiv paper introduces 3GAS-PINNs, a variant of physics-informed neural networks that uses gradient-guided Gaussian adaptive sampling to place collocation points. The method targets common weaknesses in PINNs on nonlinear partial differential equations, such as slow convergence, gradient imbalance, and poor resolution of demanding regions. By concentrating sampling where it matters most, the approach aims to improve training efficiency and solution accuracy.

pinns/pdes/adaptive-sampling/training