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DS

David Silver

04/11/25

@ Google DeepMind

Reinforcement learning involves giving a system rewards for successful actions, allowing it to learn which moves lead to winning outcomes over time.

Video

GD

Is Human Data Enough? With David Silver

@ Google DeepMind

04/11/25

Related Takeaways

MJ

Max Jaderberg

04/29/25

@ Sequoia Capital

Reinforcement learning allows us to tackle problems where we don't know the answers, focusing on whether the model's outputs are good or not, rather than needing predefined solutions.

SC

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.

SD

Sholto Douglas

05/23/25

@ Dwarkesh Patel

Reinforcement learning from verifiable rewards has significantly improved AI capabilities, especially in software engineering, where tasks can be verified easily through tests and outputs.

LF

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.

YC

Y Combinator Cast

02/06/25

@ Y Combinator

Reinforcement learning is used by both OpenAI and DeepSeek to shape the behavior of their language models based on feedback and reward signals, enhancing their usefulness and alignment.

YC

Y Combinator Cast

10/25/24

@ Y Combinator

The more reinforcement learning o1 undergoes, the more it improves, meaning that when users present complex problems, the longer o1 is allowed to think, the more accurate its responses become.

SC

Sequoia Capital Cast

02/25/25

@ Sequoia Capital

Reinforcement learning on top of models will be critical for building the most powerful AI agents, as it allows for better adaptability and performance in complex tasks.

DS

David Silver

04/11/25

@ Google DeepMind

The credit assignment problem in reinforcement learning is about determining which moves contributed to a win or loss, despite receiving only a single outcome at the end of a game.

DS

David Silver

04/11/25

@ Google DeepMind

AI systems can design their own reinforcement learning algorithms, as demonstrated by a project where an AI outperformed human-designed algorithms by learning to optimize its own learning process.