Self-Adapting Language Models

Paper, Website

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

MIT CSAIL

<img src="few-shot/assets/SEAL.png" alt="SEAL" width="400"/>

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:

• general-knowledge: 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. All experiments can be run with 2 A100/H100 GPUs. Other setups may require refactoring and/or changing model sizes.

📄 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}, 
}