An accelerated relax-and-round algorithm for concave coverage problems achieves Õ(mn ε^{-1}) runtime and a 0.827-approximation ratio for the logarithmic reward function.
How to train data-efficient llms
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KoCo conditions LLM pre-training by prepending three-dimensional semantic coordinates to documents, improving performance on 10 downstream tasks, accelerating convergence by 30%, and helping distinguish facts from noise to reduce hallucinations.
Loss-based pruning of training data to limit facts and flatten their frequency distribution enables a 110M-parameter GPT-2 model to memorize 1.3 times more entity facts than standard training, matching a 1.3B-parameter model on the full dataset.
Off-the-shelf models assess quality and alignment to select diverse multimodal training data, letting models trained on the filtered subset match or exceed full-dataset results on standard benchmarks.
Model developers must address human concerns, preferences, values, and goals with rigor at every stage of the LLM pipeline rather than only in post-training.
Safactory integrates three platforms for simulation, data management, and agent evolution to create a unified pipeline for training trustworthy autonomous AI.
Data-influence-score filtering using validation-set loss on downstream coding tasks improves Code-LLM performance, with the most beneficial training data varying significantly across different programming tasks.
This survey reviews the background, key techniques, and evaluation methods for large language models, emphasizing emergent abilities that appear at large scales.
citing papers explorer
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Accelerated Relax-and-Round for Concave Coverage Problems
An accelerated relax-and-round algorithm for concave coverage problems achieves Õ(mn ε^{-1}) runtime and a 0.827-approximation ratio for the logarithmic reward function.
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KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates
KoCo conditions LLM pre-training by prepending three-dimensional semantic coordinates to documents, improving performance on 10 downstream tasks, accelerating convergence by 30%, and helping distinguish facts from noise to reduce hallucinations.
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Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts
Loss-based pruning of training data to limit facts and flatten their frequency distribution enables a 110M-parameter GPT-2 model to memorize 1.3 times more entity facts than standard training, matching a 1.3B-parameter model on the full dataset.
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DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models
Off-the-shelf models assess quality and alignment to select diverse multimodal training data, letting models trained on the filtered subset match or exceed full-dataset results on standard benchmarks.
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Reflections and New Directions for Human-Centered Large Language Models
Model developers must address human concerns, preferences, values, and goals with rigor at every stage of the LLM pipeline rather than only in post-training.
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Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence
Safactory integrates three platforms for simulation, data management, and agent evolution to create a unified pipeline for training trustworthy autonomous AI.
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An Empirical Study on Influence-Based Pretraining Data Selection for Code Large Language Models
Data-influence-score filtering using validation-set loss on downstream coding tasks improves Code-LLM performance, with the most beneficial training data varying significantly across different programming tasks.
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A Survey of Large Language Models
This survey reviews the background, key techniques, and evaluation methods for large language models, emphasizing emergent abilities that appear at large scales.