University of Freiburg
Australian National University
Amanda AskellResearch Scientist (Alignment Finetuning) · AnthropicResearcher↗
Researcher
Jan Leike
AnthropicMember of Technical Staff
Leike has spent his career on the problem of keeping increasingly capable AI systems aligned with human intentions. He helped develop reinforcement learning from human feedback[1], co-led OpenAI's Superalignment team, and moved to Anthropic to lead work on methods for supervising systems that may become more capable than their overseers.
Career & education
Current roleAnthropicMay 2024-presentMember of Technical Staff
AnthropicMay 2024-presentPreviously
OpenAI2021-2024
Google DeepMind2016-2020
OpenAI2021-2024Google DeepMind2016-2020Work and education history is primarily focused on AI-relevant roles and may not be comprehensive.
Citation Trend
Total citations to scholarly work, including research papers, technical reports, books and theses, as recorded by Google Scholar. Each point is a dated snapshot.
citations111,223As of Nov 1, '26
citations+1,819Since last snapshot, Oct 19, '26
Selected contributions
Training Language Models to Follow Instructions with Human Feedback
With Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang and others ·InstructGPT uses human feedback to train language models to follow instructions.
GPT-4 Technical Report
With OpenAI, Joshua Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman and others ·Reported GPT-4's capabilities, limitations and safety evaluations as a text-and-image input model.
Deep reinforcement learning from human preferences
Taught AI agents tasks using people's comparisons of their behavior rather than hand-written rewards.
References
- Learning to Summarize with Human Feedbackopenai.com
Leike and colleagues trained a model to summarize text using human judgments about which summaries were better.
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Reviewed August 8, 2026 · Checked for accuracy and new information.
