Stream of Search

Repository for the paper Stream of Search: Learning to Search in Language(https://arxiv.org/abs/2404.03683)

See APA code here: https://github.com/kanishkg/RLHF-APA

Installation

1. Install conda

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

2. Create a conda environment

conda create -n sos python=3.11
conda activate sos

3. Install the required packages

pip install -r requirements.txt

Running the code

Please update the scripts in the scripts/ directory to reflect the correct paths to the data and model checkpoints. The following steps outline the process of running the code:

1. Generate the countdown dataset

sh scripts/gen_task.sh

2. Train the model

sh scripts/train.sh

3. Generating data for STaR

sh scripts/gen_star.sh

4. Train the model with STaR

sh scripts/star.sh

5. Evaluate the model

sh scripts/eval.sh

Repository Structure Overview

This repository is structured to support efficient development, training, and evaluation of models. Below is an organized breakdown of each directory:

analysis/

Purpose: Contains scripts and tools for analyzing experimental results and generating plots.

configs/

Purpose: Houses configuration files for various training settings.

• gpt-neo-s.json: For the GPT-Neo transformer model.
• oft-mix-4-cd.conf: For the Optimal Solution (OT) model.
• sft-mix-4-cd.conf: For the Stream of Search (SoS) model.
• star1-mix-4-cd.conf: For Star iteration 1 model.
• star2-mix-4-cd.conf: For Star iteration 2 model.
• star3-mix-4-cd.conf: For Star iteration 3 model.

scripts/

Purpose: Contains scripts for data generation and model training.

• gen_task.sh: Generates the initial countdown dataset.
• train.sh: Trains models under OT or SoS settings.
• gen_star.sh: Generates data for Star iterations.
• star.sh: Trains models in Star setting.
• eval.sh: Evaluates the performance of the models.

src/

Purpose: Includes all source code for model training, data generation, and evaluation.

• data.py: Generates the countdown dataset.
• model.py: Main file for model definitions.
• train.py: Executes model training processes.
• countdown.py: Generates countdown problem scenarios.
• countdown_bfs.py: Utilizes BFS for generating search streams.
• countdown_dfs.py: Utilizes DFS for generating search streams.
• countdown_utils.py: Provides utility functions for countdown scenarios.
• countdown_generate.py: Generates countdown dataset.
• countdown_optimal.py: Enriches countdown dataset with optimal paths.
• eval_neo.py: Script for model evaluation.