5 citations · 13 across the 31 of their papers we have counts for
8 papers · 1 filter
A Model with No Head and Many Thoughts
Nikita Koriagin, Yaroslav Aksenov, George Bredis +3
Large language models decode by projecting hidden states through a large vocabulary head at every step. This operation is computationally costly and forces all reasoning to be expr…
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…
Unstable Features, Reproducible Subspaces: Understanding Seed Dependence in Sparse Autoencoders
Gleb Gerasimov, Timofei Rusalev, Nikita Balagansky +3
Sparse autoencoders (SAEs) are widely used to interpret neural network representations, but their utility depends on whether the learned features are reproducible across training r…
Interpreting and Steering a Text-to-Speech Language Model with Sparse Autoencoders
Nikita Koriagin, Georgii Aparin, Nikita Balagansky +1
Language models increasingly serve as the backbone of text-to-speech (TTS) systems, yet we understand little about the representations they build when text and generated speech tok…
Trust-Region Behavior Blending for On-Policy Distillation
Daniil Plyusov, Alexey Gorbatovski, Alexey Malakhov +4
On-policy distillation (OPD) trains a student on prefixes sampled from its own policy while matching a stronger teacher. This addresses the prefix mismatch of offline distillation,…
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…
F-GRPO: Don't Let Your Policy Learn the Obvious and Forget the Rare
Daniil Plyusov, Alexey Gorbatovski, Boris Shaposhnikov +4
Reinforcement Learning with Verifiable Rewards (RLVR) is commonly based on group sampling to estimate advantages and stabilize policy updates. In practice, computational limits oft…