Scaling paradigms for large language models
Abstract :
In this talk I will tell you about the role of scaling in the past five years of artificial intelligence.
In the first scaling paradigm, which started around five years ago, our field scaled large language models by training with more compute on more data. Such scaling led to the success of ChatGPT and other AI chat engines, which were surprisingly capable and general purpose. With the release of OpenAI o1, we are at the beginning of a new paradigm where we do not just scale training time compute, but we also scale test-time compute. These new models are trained via reinforcement learning on chain-of-thought reasoning, and by thinking harder for more-challenging tasks can solve even competition-level math and programming problems.
Biodata:
Jason Wei is an AI researcher based in San Francisco. He currently works at OpenAI, where he contributed to OpenAI o1, a frontier model trained to do chain-of-thought reasoning via reinforcement learning. From 2020 to 2023, Jason was a research scientist at Google Brain, where his work popularized chain-of-thought prompting, instruction tuning, and emergent phenomena.