Michał Tyrolski

Michał Tyrolski

Senior AI Consultant at EY / Independent Researcher.

Publications

You can also find my articles on my Google Scholar profile.

Publications

OpenGVL: Benchmarking Visual Temporal Progress for Data Curation

Published in CoRL 2025 workshop, 2025

Benchmark and toolkit for evaluating VLMs’ sense of progress in robotics via Value-Order Correlation (VOC); enables automated dataset curation from videos.

Recommended citation: Budzianowski, P., Wiśnios, E., Góral, G., Tyrolski, M., Kulakov, I., Petrenko, V., & Walas, K. (2025). OpenGVL - Benchmarking Visual Temporal Progress for Data Curation. arXiv:2509.17321.
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What Matters in Hierarchical Search for Combinatorial Problems?

Published in ICLR 2024 (Generative Models for Decision Making), 2024

Empirical analysis of properties shaping hierarchical search in combinatorial reasoning; guidelines for robust comparison + future design.

Recommended citation: Zawalski, M., Góral, G., Tyrolski, M., Wiśnios, E., Budrowski, F., Cygan, M., Kuciński, Ł., & Miłoś, P. (2024). What matters in hierarchical search for combinatorial reasoning problems? arXiv preprint arXiv:2406.03361.
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Adjusting Planning Horizon with Adaptive Subgoal Search

Published in ICLR 2023 (Top-5%, Oral), 2023

AdaSubS adaptively adjusts planning horizon via diverse subgoals + fast reachability filtering; efficient on Sokoban, Rubik’s Cube, INT.

Recommended citation: Zawalski, M., Tyrolski, M., Czechowski, K., Odrzygóźdź, T., Stachura, D., Piękos, P., Wu, Y., Kuciński, Ł., & Miłoś, P. (2022). Fast and precise: Adjusting planning horizon with adaptive subgoal search. arXiv preprint arXiv:2206.00702.
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Hierarchical Transformers Are More Efficient Language Models

Published in NAACL 2022, 2022

Hourglass: a hierarchical Transformer with down/upsampling layers that improves long-sequence modeling efficiency; SOTA on ImageNet32 and strong enwik8 performance.

Recommended citation: Nawrot, P., Tworkowski, S., Tyrolski, M., Kaiser, Ł., Wu, Y., Szegedy, C., & Michalewski, H. (2021). Hierarchical transformers are more efficient language models. arXiv preprint arXiv:2110.13711.
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