Paper, Website

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

MIT CSAIL

SEAL

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.

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

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

pip install -r requirements.txt

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

OPENAI_API_KEY=your_openai_api_key_here

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.

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}, 
}