Self-Adapting Language Models

Paper, Website

Adam Zweiger, Jyothish Pari, Han Guo, Ekin Akyürek, Yoon Kim, Pulkit Agrawal

MIT CSAIL

SEAL (Self-Adapting LLMs) is a framework for training language models via RL to generate self-edits (finetuning data and other update directives for themselves) in response to new inputs.

We explore SEAL in two domains:

• knowledge-incorporation: Incorporating new factual knowledge
• few-shot: Adapting to new tasks from few-shot examples

Both folders include code, data, and documentation.

🔧 Setup

1. Clone the repository

git clone https://github.com/Continual-Intelligence/SEAL.git
cd SEAL

2. Set up a virtual environment

Using conda:

conda create -n seal_env python=3.12
conda activate seal_env

Using venv:

python3.12 -m venv seal_env
source seal_env/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Configure environment

Create a .env file in the project root and add your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key_here

5. SLURM users

Before running any shell scripts, make sure to update the SLURM directives at the top of each .sh file to match your system configuration.

📄 Citation

If you found this work useful, please cite:

@misc{zweiger2025selfadaptinglanguagemodels,
      title={Self-Adapting Language Models}, 
      author={Adam Zweiger and Jyothish Pari and Han Guo and Ekin Akyürek and Yoon Kim and Pulkit Agrawal},
      year={2025},
      eprint={2506.10943},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2506.10943}, 
}