Greg Durrett
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VESTA introduces a novel agent framework that enhances Visual Language Models (VLMs) by equipping them with a dynamic, reusable toolkit of diagnostic and statistical tools, significantly improving automated quantitative model fitting on complex scientific data.
The paper proposes Randomized YaRN, a training method that improves length generalization in large language models by exposing them to out-of-distribution positional representations during training on short context data.
Papers
Randomized YaRN Improves Length Generalization for Long-Context Reasoning
The paper proposes Randomized YaRN, a training method that improves length generalization in large language models by exposing them to out-of-distribution positional representations during training on…