Discussion on Google Deepmind & Princeton's 'Tree of Thoughts' method

Join us as we explore Google Deepmind and Princeton University's innovative "Tree of Thoughts" (ToT) approach, enhancing language model inference. We'll delve into ToT's strategic decision-making capabilities and improved problem-solving prowess, highlighting its significant success in complex tasks like the Game of 24. Your questions and insights are welcome.

May 24, 2023, 7:00 – 8:00 PM


Key Themes


About this event

Join us for our upcoming meetup. We're going to be diving into the next big thing in language model inference - the "Tree of Thoughts" (ToT) approach developed by Google Deepmind and Princeton.

Conventional language models have long been hindered by their token-level, left-to-right decision-making processes during inference. This restriction often left them lacking in tasks demanding strategic foresight, exploration, or when initial decisions significantly influenced the outcomes. The ToT model aims to overcome these limitations, equipping language models with the ability to explore and evaluate multiple reasoning paths, enabling them to make informed strategic decisions and improve their problem-solving capabilities.

In this event, we'll take a closer look at how ToT has been applied to tasks that call for complex planning or searching. A prime example is its remarkable performance in the Game of 24 task, where it significantly surpassed the success rates of traditional methods.

Here is the Arxiv link for the paper we'll be discussing: https://arxiv.org/pdf/2305.10601.pdf

Bring your questions, thoughts, and be ready for an exciting conversation. Looking forward to seeing you there!

For those who may not be able to attend, rest assured, we'll have the event recorded. Do not hesitate to get in touch if you wish to watch the event at a later time.


  • Leopoldo Hernandez


    GDG Organizer

  • Daniel Goncharov

    GDG Organizer

    Head of AI lab

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