Search Results for author: Łukasz Kuciński

Found 20 papers, 11 papers with code

RoboMorph: Evolving Robot Morphology using Large Language Models

no code implementations11 Jul 2024 Kevin Qiu, Krzysztof Ciebiera, Paweł Fijałkowski, Marek Cygan, Łukasz Kuciński

We introduce RoboMorph, an automated approach for generating and optimizing modular robot designs using large language models (LLMs) and evolutionary algorithms.

Evolutionary Algorithms Navigate

What Matters in Hierarchical Search for Combinatorial Reasoning Problems?

1 code implementation5 Jun 2024 Michał Zawalski, Gracjan Góral, Michał Tyrolski, Emilia Wiśnios, Franciszek Budrowski, Łukasz Kuciński, Piotr Miłoś

Efficiently tackling combinatorial reasoning problems, particularly the notorious NP-hard tasks, remains a significant challenge for AI research.

tsGT: Stochastic Time Series Modeling With Transformer

no code implementations8 Mar 2024 Łukasz Kuciński, Witold Drzewakowski, Mateusz Olko, Piotr Kozakowski, Łukasz Maziarka, Marta Emilia Nowakowska, Łukasz Kaiser, Piotr Miłoś

Time series methods are of fundamental importance in virtually any field of science that deals with temporally structured data.

Time Series

GUIDE: Guidance-based Incremental Learning with Diffusion Models

1 code implementation6 Mar 2024 Bartosz Cywiński, Kamil Deja, Tomasz Trzciński, Bartłomiej Twardowski, Łukasz Kuciński

We introduce GUIDE, a novel continual learning approach that directs diffusion models to rehearse samples at risk of being forgotten.

Continual Learning Incremental Learning

Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem

no code implementations5 Feb 2024 Maciej Wołczyk, Bartłomiej Cupiał, Mateusz Ostaszewski, Michał Bortkiewicz, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś

Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models.

Montezuma's Revenge NetHack +2

Structured Packing in LLM Training Improves Long Context Utilization

no code implementations28 Dec 2023 Konrad Staniszewski, Szymon Tworkowski, Sebastian Jaszczur, Yu Zhao, Henryk Michalewski, Łukasz Kuciński, Piotr Miłoś

Recent advancements in long-context large language models have attracted significant attention, yet their practical applications often suffer from suboptimal context utilization.

Information Retrieval Retrieval

Magnushammer: A Transformer-Based Approach to Premise Selection

no code implementations8 Mar 2023 Maciej Mikuła, Szymon Tworkowski, Szymon Antoniak, Bartosz Piotrowski, Albert Qiaochu Jiang, Jin Peng Zhou, Christian Szegedy, Łukasz Kuciński, Piotr Miłoś, Yuhuai Wu

By combining \method with a language-model-based automated theorem prover, we further improve the state-of-the-art proof success rate from $57. 0\%$ to $71. 0\%$ on the PISA benchmark using $4$x fewer parameters.

Automated Theorem Proving Language Modelling +1

Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery

no code implementations NeurIPS 2023 Mateusz Olko, Michał Zając, Aleksandra Nowak, Nino Scherrer, Yashas Annadani, Stefan Bauer, Łukasz Kuciński, Piotr Miłoś

In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function.

Causal Discovery Experimental Design

Disentangling Transfer in Continual Reinforcement Learning

no code implementations28 Sep 2022 Maciej Wołczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś

The ability of continual learning systems to transfer knowledge from previously seen tasks in order to maximize performance on new tasks is a significant challenge for the field, limiting the applicability of continual learning solutions to realistic scenarios.

Continual Learning Continuous Control +2

Continual World: A Robotic Benchmark For Continual Reinforcement Learning

1 code implementation NeurIPS 2021 Maciej Wołczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś

Continual learning (CL) -- the ability to continuously learn, building on previously acquired knowledge -- is a natural requirement for long-lived autonomous reinforcement learning (RL) agents.

Continual Learning reinforcement-learning +1

Emergence of compositional language in communication through noisy channel

no code implementations ICML Workshop LaReL 2020 Łukasz Kuciński, Paweł Kołodziej, Piotr Miłoś

In this paper, we investigate how communication through a noisy channel can lead to the emergence of compositional language.

Uncertainty-sensitive Learning and Planning with Ensembles

1 code implementation19 Dec 2019 Piotr Miłoś, Łukasz Kuciński, Konrad Czechowski, Piotr Kozakowski, Maciek Klimek

The former manifests itself through the use of value function, while the latter is powered by a tree search planner.

Montezuma's Revenge

Uncertainty - sensitive learning and planning with ensembles

1 code implementation25 Sep 2019 Piotr Miłoś, Łukasz Kuciński, Konrad Czechowski, Piotr Kozakowski, Maciej Klimek

Notably, our method performs well in environments with sparse rewards where standard $TD(1)$ backups fail.

Montezuma's Revenge

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