--- datasets: - zeweizhang/ImitSAT-KeyTrace language: - en license: apache-2.0 pipeline_tag: other tags: - imitation-learning - boolean-satisfiability - SAT - CDCL - branching-policy - perceiver-ar - autoregressive - decision-sequence ---
More details, training/evaluation scripts, and ablations can be found in the GitHub repository and the paper.
## Introduction ImitSAT is a branching policy for conflict-driven clause learning (CDCL) solvers based on imitation learning for the Boolean satisfiability problem (SAT). Unlike previous methods that predict instance-level signals, ImitSAT learns from expert **KeyTrace**—a sequence of surviving decisions from a full solver run. This prefix-conditioned supervision enables ImitSAT to reproduce high-quality branches, reducing propagations and wall-clock time. ## Download the model ```bash git lfs install git clone https://huggingface.co/zeweizhang/ImitSAT ``` ## Files in this repo ``` ImitSAT.npz — trained checkpoint (NumPy/JAX arrays). tokenizer/ vocab.txt tokenizer_config.json special_tokens_map.json ``` ## Citation ```bibtex @inproceedings{zhang2026boolean, title={Boolean Satisfiability via Imitation Learning}, author={Zewei Zhang and Huan Liu and YUANHAO YU and Jun Chen and Xiangyu Xu}, booktitle={The Fourteenth International Conference on Learning Representations}, year={2026}, url={https://openreview.net/forum?id=LNqWbY5iIf} } ```