Share your thoughts, 1 month free Claude Pro on usSee more
WorkDL logo mark

Phi-4 Technical Report

About

We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models, where pre-training is based primarily on organic data sources such as web content or code, phi-4 strategically incorporates synthetic data throughout the training process. While previous models in the Phi family largely distill the capabilities of a teacher model (specifically GPT-4), phi-4 substantially surpasses its teacher model on STEM-focused QA capabilities, giving evidence that our data-generation and post-training techniques go beyond distillation. Despite minimal changes to the phi-3 architecture, phi-4 achieves strong performance relative to its size -- especially on reasoning-focused benchmarks -- due to improved data, training curriculum, and innovations in the post-training scheme.

Marah Abdin, Jyoti Aneja, Harkirat Behl, S\'ebastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J. Hewett, Mojan Javaheripi, Piero Kauffmann, James R. Lee, Yin Tat Lee, Yuanzhi Li, Weishung Liu, Caio C. T. Mendes, Anh Nguyen, Eric Price, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Xin Wang, Rachel Ward, Yue Wu, Dingli Yu, Cyril Zhang, Yi Zhang• 2024

Related benchmarks

TaskDatasetResultRank
Commonsense ReasoningWinoGrande--
1442
Instruction FollowingIFEval--
836
Language ModelingWikiText
PPL9.59
740
ReasoningBBH
Accuracy87.6
726
Instruction FollowingAlpacaEval 2.0--
722
Multi-hop Question Answering2WikiMultihopQA--
559
Mathematical ReasoningGSM8K
Accuracy (GSM8K)89.4
358
Mathematical ReasoningAIME 2025
Accuracy36.67
214
ReasoningHellaSwag (HS)
HellaSwag Accuracy87.62
209
Mathematical ReasoningAIME 2024 (test)
Accuracy10
209
Showing 10 of 157 rows
...

Other info

Follow for update