4 papers · 1 filter
Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Inference
Ruslan Rakhimov, George Bredis, Yuriy Maksyuta +1
Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels. Published JEPA world models for pixel-based control (…
Rank-Then-Act: Reward-Free Control from Frame-Order Progress
Yuriy Maksyuta, George Bredis, Ruslan Rakhimov +1
We introduce Rank-Then-Act (RTA), a framework for learning control policies from expert video demonstrations without environment rewards. RTA trains a Vision-Language Model (VLM) o…
Next Embedding Prediction Makes World Models Stronger
George Bredis, Nikita Balagansky, Daniil Gavrilov +1
Capturing temporal dependencies is critical for model-based reinforcement learning (MBRL) in partially observable, high-dimensional domains. We introduce NE-Dreamer, a decoder-free…
Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success
George Bredis, Stanislav Dereka, Viacheslav Sinii +2
Interactive multimodal agents must convert raw visual observations into coherent sequences of language-conditioned actions -- a capability that current vision-language models (VLMs…