🔸 Issue #16: Monte Carlo Tree Search
Plus: SSI - a new startup by Ilya Sutskever and MAKE: Bootstrapper's Handbook" a practical guide for building successful startups and products
🗒️ IN TODAY’S ISSUE
🔸 “Monte Carlo Tree Search” from the paper “Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B”
👨🏻💻 SSI. - a new startup co-founded by Ilya Sutskever.
🧠 "MAKE: Bootstrapper's Handbook" a practical guide for building successful startups and products.
🔸 Extract #1: Monte Carlo Tree Search
from the paper “Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B”
The paper introduces a novel algorithm called Monte Carlo Tree Self-Refine (MCTSr), designed to improve the performance of LLMs like GPT-4 and LLaMA in solving complex mathematical problems, such as those found in mathematical Olympiads. The MCTSr algorithm combines the strengths of Monte Carlo Tree Search (MCTS), a decision-making tool that systematically explores possible outcomes to make optimal decisions, with the self-refinement capabilities of LLMs, which iteratively improve their answers through feedback and evaluation.
This integration aims to address the accuracy and reliability issues often encountered in LLM-generated solutions for strategic and mathematical reasoning tasks.
The MCTSr algorithm works by constructing a search tree where nodes represent different versions of potential answers, and edges represent attempts to refine these answers. The process involves several key phases: selecting promising nodes, expanding the search tree with new potential answers, simulating outcomes to evaluate these answers, and backpropagating the results to update the tree's values.
Monte Carlo Tree Search (MCTS) is an algorithm used to find optimal decisions in games and other decision-making problems. It combines a random sampling of possible future states with a tree search to efficiently explore promising paths.
The key idea is to simulate many random games from the current position, building a search tree where each node represents a game state.
The value of each node is the average outcome of all simulated games that visit that state.MCTS has four main phases:
Selection: Starting from the root node, child nodes are selected based on a formula that balances exploitation of high-value nodes with exploration of less-visited nodes. This continues until an unvisited node is reached.
Expansion: One or more child nodes are added to expand the tree from the selected node.
Simulation: A random playout is performed from the new node, simulating the game to completion using random actions. The outcome (win, loss, draw) is recorded.
Backpropagation: The simulation outcome is propagated back through the selected nodes all the way to the root, updating the win/loss statistics.
After many iterations, the tree focuses on high-value areas of the search space. The algorithm can then select the best move by choosing the child of the root with the highest win ratio.
MCTS is very effective at games with large branching factors like Go, where it outperforms traditional search algorithms. It has also been applied to other domains like robotics and cybersecurity.
The key advantage is its ability to focus search on promising areas and learn from random simulations without requiring a domain-specific evaluation function.
Let’s explain it like a treasure hunt story:
Imagine you're a pirate searching for buried treasure on a mysterious island. You have a map that shows the general area where the treasure is hidden, but the exact location is unknown.
To find the treasure, you'll use the MCTS approach:
1. Exploration
You start at the "X" on the map, which represents your current position. From here, you can choose different paths to explore the island.
2. Random Sampling
Since you don't know the exact location, you decide to randomly wander the island, taking different routes and seeing where they lead. Each time you reach a new spot, you make a note of whether you found any treasure.
3. Building the Tree
As you explore, you start to build a mental "tree" of all the paths you've taken. The root of the tree is your starting point, and each branch represents a different route you've tried.
4. Evaluating the Paths
After many random wanderings, you start to notice that some paths seem to lead to more treasure than others. You assign a "value" to each branch of the tree based on how much treasure you've found along that route.
5. Focusing the Search
Armed with this information, you start to focus your exploration on the branches of the tree that have the highest treasure values. You spend more time searching those areas, while still occasionally trying new paths to see if you can find an even better route.
6. Finding the Treasure
As you repeat this process, your tree becomes more and more refined, with the high-value branches standing out clearly. Eventually, you're able to identify the single best path to the buried treasure and dig it up, claiming your prize!
Just like a pirate searching for treasure, MCTS uses a combination of random exploration and strategic decision-making to efficiently find the optimal solution in complex, uncertain environments.
👨🏻💻 AI Startup
SSI. - a new startup co-founded by Ilya Sutskever.
“It’s called Safe Superintelligence Inc. SSI is our mission, our name, and our entire product roadmap, because it is our sole focus. Our team, investors, and business model are all aligned to achieve SSI. We approach safety and capabilities in tandem, as technical problems to be solved through revolutionary engineering and scientific breakthroughs. We plan to advance capabilities as fast as possible while making sure our safety always remains ahead. This way, we can scale in peace.” says on their website, and X. I am anxiously anticipating their first product from this exciting new venture and competitor.
🧠 Article
The book "MAKE: Bootstrapper's Handbook" by Pieter Levels (@levelsio) is a practical guide for building successful startups and products.
Some key points:
Levels shares his own experiences of building profitable online businesses like Nomad List and Remote OK, which have generated millions in revenue.
The book emphasizes finding ideas by solving your problems, and then building and launching products quickly to get feedback from users.
Levels wrote the book in public, live-streaming the writing process and sending draft chapters to pre-order customers for feedback.
The book covers the entire startup journey - from ideation to building, launching, and monetizing products. It provides concrete strategies like launching an API to grow a user base.
Many readers credit the book as a turning point that inspired them to start their own successful solo projects and businesses.
Until next week,
Nino.