How OpenAI Uses Fine-tuning
6 engineering articles about Fine-tuning from OpenAI's engineering team
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The article discusses emergent misalignment in large language models, particularly focusing on how misaligned persona features can lead to generalized misalignment.
OpenAI Team
16 min read
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The article discusses the development of a neural network capable of playing Minecraft through a method called Video PreTraining (VPT), leveraging a large dataset of unlabeled gameplay videos.
Bowen Baker
8 min read
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The article discusses the lessons learned from deploying language models, focusing on safety and misuse.
Miles Brundage
14 min read
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The article discusses the application of reinforcement learning from human feedback to enhance the summarization capabilities of language models.
Nisan Stiennon
16 min read
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The article discusses the fine-tuning of the 774M parameter GPT-2 language model using human feedback to improve performance on various natural language tasks, including summarization and stylistic...
Daniel Ziegler
9 min read
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The article discusses the advancements in language models, particularly focusing on GPT-2, which generates coherent text and performs various language tasks without task-specific training.
Alec Radford
12 min read
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