Researcher
Jeffrey Wu
Current role not listed
Wu has worked on scaling language models and learning from human feedback at OpenAI and Anthropic, including early work on summarization with human preferences. At Anthropic, he also worked on interpretability: studying how AI models work internally.[1]
Anthropic
OpenAI
Google
MITCitation Trend
Updates 1
Selected contributions
Language Models are Few-Shot Learners
With Tom Brown, Ben Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan and others ·GPT-3 performs tasks from prompts and a few examples without task-specific training.
Training Language Models to Follow Instructions with Human Feedback
With Long Ouyang, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal and others ·InstructGPT uses human feedback to train language models to follow instructions.
Language Models are Unsupervised Multitask Learners
GPT-2 showed how learning to predict text could support tasks without separate training.
References
- New York Magazine / Asterisk: AI researchers on why they quitnymag.com
Wu describes working on interpretability when discussing leaving Anthropic. Interview read directly on 2026-10-17.
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Reviewed July 23, 2026 · Checked for accuracy and new information.