6 papers
torchtune: PyTorch native post-training library
Mark Obozov, Maxime Griot, Joseph Cummings +8
Modern LLMs typically require multistage training pipelines to achieve strong downstream performance, with post-training serving as the main interface for adapting open-weight mode…
CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs
A. Chervov, M. Obozov, A. Soibelman +31
This paper is the second in a series of studies on developing efficient artificial intelligence-based approaches to pathfinding on extremely large graphs (e.g. nodes) wit…
CayleyPy Growth: Efficient growth computations and hundreds of new conjectures on Cayley graphs (Brief version)
A. Chervov, D. Fedoriaka, E. Konstantinova +46
This is the third paper of the CayleyPy project applying artificial intelligence to problems in group theory. We announce the first public release of CayleyPy, an open source Pytho…
CayleyPy-4: AI-Holography. Towards analogs of holographic string dualities for AI tasks
A. Chervov, F. Levkovich-Maslyuk, A. Smolensky +41
This is the fourth paper in the CayleyPy project, which applies AI methods to the exploration of large graphs. In this work, we suggest the existence of a new discrete version of h…
Exploring Applications of State Space Models and Advanced Training Techniques in Sequential Recommendations: A Comparative Study on Efficiency and Performance
Mark Obozov, Makar Baderko, Stepan Kulibaba +2
Recommender systems aim to estimate the dynamically changing user preferences and sequential dependencies between historical user behaviour and metadata. Although transformer-based…
A Machine Learning Approach That Beats Large Rubik's Cubes
Alexander Chervov, Kirill Khoruzhii, Nikita Bukhal +9
The paper proposes a novel machine learning-based approach to the pathfinding problem on extremely large graphs. This method leverages diffusion distance estimation via a neural ne…