This short tutorial explains the training objectives used to develop ChatGPT, the new chatbot language model from OpenAI.
Timestamps:
0:00 - Non-intro
0:24 - Training overview
1:33 - Generative pretraining (the raw language model)
4:18 - The alignment problem
6:26 - Supervised fine-tuning
7:19 - Limitations of supervision: distributional shift
8:50 - Reward learning based on preferences
10:39 - Reinforcement learning from human feedback
13:02 - Room for improvement
ChatGPT: openai.com/blog/chatgpt
Relevant papers for learning more:
InstructGPT: Ouyang et al., 2022 - arxiv.org/abs/2203.02155
GPT-3: Brown et al., 2020 - arxiv.org/abs/2005.14165
PaLM: Chowdhery et al., 2022 - arxiv.org/abs/2204.02311
Efficient reductions for imitation learning: Ross & Bagnell, 2010 - proceedings.mlr.press/v9/ross...
Deep reinforcement learning from human preferences: Christiano et al., 2017 - arxiv.org/abs/1706.03741
Learning to summarize from human feedback: Stiennon et al., 2020 - arxiv.org/abs/2009.01325
Scaling laws for reward model overoptimization: Gao et al., 2022 - arxiv.org/abs/2210.10760
Proximal policy optimization algorithms: Schulman et al., 2017 - arxiv.org/abs/1707.06347
Special thanks to Elmira Amirloo for feedback on this video.
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8 июн 2024
