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New research outlines scaling laws for OpenEuroLLM models

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv details the derivation of scaling laws for OpenEuroLLM models, focusing on learning rate, batch size, and loss. The study investigates how these parameters evolve with model capacity and data scale, proposing a model to capture these relationships. It also examines the benefits of learning rate annealing and the transferability of optimal learning rates between different phases of the schedule. The research establishes a baseline and procedure for developing future OpenEuroLLM models and makes the pretraining runs publicly available. AI

IMPACT Establishes a baseline for developing future large language models and provides open-source pretraining data.

RANK_REASON The cluster contains a research paper detailing scaling laws for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research outlines scaling laws for OpenEuroLLM models

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The cluster contains a research paper detailing scaling laws for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Niccol\`o Ajroldi, Diana Alexandra Onutu, Haider Al-Tahan, J\"org Franke, Sampo Pyysalo, Jenia Jitsev, Aaron Klein ·

    Deriving Scaling Laws for OpenEuroLLM Models: Learning Rate, Batch Size and Loss

    arXiv:2608.28308v1 Announce Type: new Abstract: We study the scaling behavior of learning rate and batch size in pretraining dense large language models on English-prevalent corpora. Beyond scaling \textit{jointly optimal} learning rates and batch sizes, we investigate their \tex…

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