NeurIPS 2025 E2LM Competition — Early Training Evaluation of Language Models with lm-evaluation-harness

AI Impact Summary

NeurIPS 2025 E2LM Competition introduces a formal benchmark for extracting meaningful early-stage signals from LLM training, focusing on scientific knowledge without relying on full convergence. The initiative uses the lm-evaluation-harness framework and is hosted via Hugging Face Spaces, with accessible run-time on free-tier Google Colab GPUs, lowering barriers to participation. Scoring combines signal quality, ranking consistency, and compliance with scientific knowledge, plus leakage checks to ensure integrity, which will force teams to design experiments that produce robust early signals rather than overfit to later-stage performance. By standardizing these early-training metrics, organizations can compare architectures and data mixtures earlier in development, reducing wasted compute and accelerating iteration cycles.