about
EXAONE Deep: Reasoning Enhanced Language Models (arxiv.org)
2 points by saikatsg on Mar 19, 2025 | hide | past | pdf | discuss on HN

In plain words: These language models are trained mostly on math and coding problems worked out in long, step-by-step thinking traces, so they can reason before answering. The smaller versions beat same-size rivals, and the largest holds its own against the best openly shared models.

Abstract

We present EXAONE Deep series, which exhibits superior capabilities in various reasoning tasks, including math and coding benchmarks. We train our models mainly on the reasoning-specialized dataset that incorporates long streams of thought processes. Evaluation results show that our smaller models, EXAONE Deep 2.4B and 7.8B, outperform other models of comparable size, while the largest model, EXAONE Deep 32B, demonstrates competitive performance against leading open-weight models. All EXAONE Deep models are openly available for research purposes and can be downloaded from https://huggingface.co/LGAI-EXAONE.

Kyunghoon Bae, Eunbi Choi, Kibong Choi, Stanley Jungkyu Choi, Yemuk Choi, Seokhee Hong, Junwon Hwang, Hyojin Jeon, Kijeong Jeon, Gerrard Jeongwon Jo, Hyunjik Jo, Jiyeon Jung, et al.
arXiv:2503.12524 · cs.CL, cs.AI · submitted Mar 16, 2025 · updated Jan 2, 2026
abstract · pdf · html

add comment on HN