GitHub - VikhrModels/mctslib

mcts-llm

MCTSr

Based on Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B by Zhang, et al.

At a high level, MCTSr iteratively generates solutions to a specified (math) problem.

In a MCTSr tree, nodes correspond to attempted answers, and edges correspond to attempts to improve the answer.

Initialize

Generate an solution to the problem. This paper uses a "dummy" solution (e.g. "I don't know").

Select a node to expand

We gather a set of candidate nodes which haven't been fully expanded.

A node is fully expanded if either:

1. it has max_children
2. any of its children have a Q value which is greater than its own

Once we've gathered the candidates, we compute UCT scores for each candidate node. There are a few ways we can make our selection:

1. Greedily (choose the node with the highest UCT)
2. Importance sampling (sample from the set of candidates, weighted by their UCT score)
3. Pairwise importance sampling (sample the max from a pair of nodes from the set of candidates, weighted by the difference between the pair's UCT scores)

The authors mention that they perform greedy selection in the paper. In their repo, they also perform pairwise sampling and save the (question, answer1, answer2) tuples for use in DPO.

Expand the node

Expansion involves several steps:

1. Generate a critique of the current solution.
2. Refine the solution based on the critique.
3. Add a new child, corresponding to the refined solution.
4. Self-evaluate the reward of the new child.
5. Backpropagate the reward from the new child through its parents, through to the root.

Results

How to use

1. Clone the repository (assuming the repository URL is provided):

   git clone https://github.com/VikhrModels/mctslib.git
   cd mctslib

2. Install dependencies (from a requirements.txt file):

   pip install -r requirements.txt

import os
from src.mcts_llm.mctsr import MCTSrLlama38B
from src.dataset_utils import load_aime
from tqdm import tqdm
import pandas as pd
from src.mcts_llm.mctsr import print_tree

# Set your API key
os.environ['TOGETHER_API_KEY'] = '<your key>'

# Define the problem/question
q = """Your Task"""

# Initialize the MCTS model with the given parameters
mctsr = MCTSrLlama38B(
    problem=q,
    max_rollouts=4,
    max_children=8,
    selection_policy=2,
    initialize_strategy=2
)

# Run the MCTS model
best_answer = mctsr.run()

# Print the best answer found
print(best_answer)

• max_rollouts=8
• max_children=2