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Lex Fridman Cast

02/03/25

@ Lex Fridman

There's surprising evidence that once you set up ways to collect verifiable data, language models can learn from sparse rewards, even if they are very weak models.

Video

DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459

@ Lex Fridman

DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459

02/03/25

Related Takeaways

Lex Fridman Cast

02/03/25

@ Lex Fridman

The idea behind reinforcement learning is that you can learn from very sparse rewards, which applies to both language generation and tasks for robots.

Sequoia Capital Cast

02/25/25

@ Sequoia Capital

Reinforcement learning is experiencing a resurgence due to advancements in language models, making it ripe for tuning to specific use cases with defined reward functions.

Trenton Bricken

05/23/25

@ Dwarkesh Patel

The challenge with language models is that while they can learn from feedback, they often lack the ability to generalize effectively across different tasks without specific scaffolding.

Sequoia Capital Cast

06/03/25

@ Sequoia Capital

The early days of AI development were marked by skepticism about the viability of language models, but persistence in technology has proven their value.

Lex Fridman Cast

02/03/25

@ Lex Fridman

Even small models can boost their performance in tasks like math with a small amount of reinforcement learning training.

Y Combinator Cast

01/24/25

@ Y Combinator

By allowing models to think longer, large language models can leverage more compute on the fly, enhancing their intelligence for complex problems.

LangChain Cast

05/30/25

@ LangChain

Using large language models as judges, combined with human review, allows for scaling without overwhelming human subject matter experts.

Raza Habib

03/01/23

@ Y Combinator

The new technology of large language models is creating unprecedented opportunities for startups, enabling tasks that previously required extensive research teams to be accomplished more easily.

Jeff Dean

08/08/17

@ Y Combinator

We can achieve higher accuracy in translations by training larger models on extensive datasets, potentially exceeding human quality for some language pairs.