3 sources
A high-signal person to study if you care about the practical mechanics of adapting large models, especially where scaling theory turns into techniques that actually spread across the industry.
Start HereAbout | Edward Hu
Researcher Profile
Editor reviewedYuanzhi Li
Parameter-efficient finetuning
Carnegie Mellon machine learning professor and LoRA coauthor
A useful profile for the seam between deep-learning theory and practical large-model methods, especially if you want someone whose work spans convergence theory, small-language-model data design, and LoRA.
Organizations
Carnegie Mellon University
Topics
About This Page
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LoRA
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TinyStories
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Deep-learning theory
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Parameter-efficient finetuning
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LoRA: Low-Rank Adaptation of Large Language Models
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Finetuning
Start Here
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Related Researchers
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3 sources
Useful because his work spans the older machine-comprehension era at Microsoft and the later LoRA-style adaptation line that became core infrastructure for modern finetuning.
3 sources
A useful profile for the path from parameter-efficient finetuning into newer agent-safety work, especially if you want people whose contributions span both model customization and tool-using systems security.
3 sources
One of the clearer people to follow if you want the bridge between deep-learning theory, practical adaptation methods like LoRA, and broader attempts to explain how large language models actually work.
Start HereZeyuan Allen-Zhu's Home Page
1 source
Co-authored LoRA: one of the core techniques behind modern fine-tuning pipelines.
1 source
Co-authored LoRA: one of the core techniques behind modern fine-tuning pipelines.