Boyan Gao

Boyan Gao has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, Efficient models & compression; often with Bo-Wen Zhang, Guang Liu, Haokun Lin.

2 papers in our library · 2024–2026

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Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

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Aquila2 Technical Report (2024 · 1 citation)
Most LLMs are pretrained on a fixed dataset over a long run, so it is hard to change the data mix or add new sources partway through. BAAI's Aquila2 is a family of Chinese–English bilingual models at 7B, 34B and an experimental 70B size. They were trained with HeuriMentor (HM), a framework with three parts:
EfficientLLM: Unified Pruning-Aware Pretraining for Auto-Designed Compact Language Models (2026)
Small language models meant for phones and other edge devices are usually trained from scratch. Their quality is then limited by parameter scaling laws, while standard compression of a large model loses a lot of accuracy because pruning uses only a small calibration set. The authors present EfficientLLM, a framework…

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Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read).

Updated 2 November 2026