# Announcing NeurIPS 2025 E2LM Competition: Early Training Evaluation of Language Models

DevFeed: [Announcing NeurIPS 2025 E2LM Competition: Early Training Evaluation of Language Models](<https://devfeed.tech/articles/announcing-neurips-2025-e2lm-competition-early-training-evaluation-of-language-models-7504.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/tiiuae/e2lm-competition>)

Author: Mouadh Yagoubi; Yasser Dahou; Billel Mokeddem; Younes B; Phúc Lê Khắc; Basma Boussaha; Ralami; Jingwei Zuo; Mughaira

Published: 2025-07-04T12:25:00Z

Content type: news

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [.NET Conf](<https://devfeed.tech/topics/net-conf.md>)

Tags: [2025](<https://devfeed.tech/tags/2025.md>), [architectures](<https://devfeed.tech/tags/architectures.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [blog](<https://devfeed.tech/tags/blog.md>), [competition](<https://devfeed.tech/tags/competition.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [google](<https://devfeed.tech/tags/google.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [models](<https://devfeed.tech/tags/models.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [technology](<https://devfeed.tech/tags/technology.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training](<https://devfeed.tech/tags/training.md>), [validation](<https://devfeed.tech/tags/validation.md>), [verification](<https://devfeed.tech/tags/verification.md>)

## AI overview

The article announces the NeurIPS 2025 E2LM Competition, which seeks new benchmarks for evaluating early-stage training of Large Language Models on scientific knowledge. Participants will submit solutions based on the lm-evaluation-harness library, with submissions scored for signal quality, ranking consistency, and alignment with scientific knowledge.

## Source excerpt

A Blog post by Technology Innovation Institute on Hugging Face