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Reinforcement learning from human feedback (RLHF) is the canonical framework for large language model alignment.
Problems of monetary management: the UK experience
Charles AE Goodhart · 1984
Earlier work this paper cites.
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Earlier work this paper cites.
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Deep reinforcement learning from human preferences
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Improving language understanding by generative pre-training
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Earlier work this paper cites.
Off-policy deep reinforcement learning without exploration
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Earlier work this paper cites.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
Earlier work this paper cites.
Star: Bootstrapping reasoning with reasoning
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A general theoretical paradigm to understand learning from human preferences
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URL https://www.anthropic.com/news/claude-3-family
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Human alignment of large language models through online preference optimisation
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Direct preference optimization: Your language model is secretly a reward model
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Llama: Open and efficient foundation language models
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Preference fine-tuning of llms should leverage suboptimal, on-policy data
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Generalized preference optimization: A unified approach to offline alignment
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URL https://cloud.google.com/vertex-ai/generative-ai/docs/models/side-by-side-eval#autosxs
Vertex, 2024 · 2024
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Is dpo superior to ppo for llm alignment? a comprehensive study
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