STATUS: This codebase is not well tested and does not use the exact same settings we used in the paper, but in our experience gives qualitatively similar results when using large model size gaps and multiple seeds. Expected results can be found for two datasets below.

This project contains code for implementing our paper on weak-to-strong generalization.

The primary codebase contains a re-implementation of our weak-to-strong learning setup for binary classification tasks. The codebase contains code for fine-tuning pretrained language models, and also training against the labels from another language model. We support various losses described in the paper as well, such as the confidence auxiliary loss.

The vision directory contains stand-alone code for weak-to-strong in the vision models setting (AlexNet -> DINO on ImageNet).

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

You need to have Python installed on your machine. The project uses pyproject.toml to manage dependencies. To install the dependencies, you can use a package manager like pip:

pip install .

The main script of the project is sweep.py. It can be run from the command line using the following command:

python sweep.py --model_sizes=gpt2,gpt2-medium

In addition to --model_sizes, sweep.py takes in almost all of the arguments that train_simple.py takes (e.g. --batch_size, --n_docs, --n_test_docs etc., see train_simple.py for a full list). These arguments are simply forwarded to train_simple.py.

sweep.py calls train_simple.py in the following way:

1. First, it calls train_simple.py for each model size to train the ground truth models
2. Then, for each pair of weak and strong models in model_sizes (where a model can be the strong model in the pair only if its index in the model_sizes list is >= the index of the weak model), it calls train_simple.py with a --weak_model_size argument so that the strong model is trained with the labels of the weak model.

E.g. the example above will run gpt2 (ground truth), gpt2-medium (ground truth), gpt2 -> gpt2, gpt2 -> gpt2-medium, and gpt2-medium -> gpt2-medium.

If needed, you can also run train_simple.py directly.

Note that sweep.py will not accept the arguments --weak_model_size, --weak_labels_path or --model_size (as opposed to --model_sizes, with an "s") as choosing their values automatically is precisely the point of sweep.py.

An example of Jupyter notebook for plotting results is found in notebooks/Plotting.ipynb.

At the time of release, the main script was called train_weak_to_strong.py, but it was less usable than sweep.py and train_simple.py. It is preserved here and the old instructions are given at the end of the document.

• Adrien Ecoffet
• Manas Joglekar
• Jeffrey Wu
• Jan Hendrik Kirchner
• Pavel Izmailov (vision)

This project is licensed under the MIT License - see the LICENSE.md file for details.

• Hugging Face for their open-source transformer models

You can run the original training script using:

python train_weak_to_strong.py

The script accepts several command-line arguments to customize the training process. Here are some examples:

python train_weak_to_strong.py --batch_size 32 --max_ctx 512 --ds_name "sciq" --loss "logconf" --n_docs 1000 --n_test_docs 100 --weak_model_size "gpt2-medium" --strong_model_size "gpt2-large" --seed 42

The notebook notebooks/Plotting_old.ipynb preserves the plotting notebook corresponding to old style training.

The key difference between this style and the new sweep.py style is that train_weak_to_strong.py will always train three models: a weak model, a transfer model, and a strong model. sweep.py optimizes this by training a series of ground truth models (which will serve as weak and strong models) as well as a series of transfer models all in one go. This reduces training duplication and is arguably simpler. The files generated by train_simple.py and sweep.py are also simpler to use.