2 citations · 2 across the 11 of their papers we have counts for
11 papers
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…
Small Vectors, Big Effects: A Mechanistic Study of RL-Induced Reasoning via Steering Vectors
Viacheslav Sinii, Nikita Balagansky, Gleb Gerasimov +6
The mechanisms by which reasoning training reshapes LLMs' internal computations remain unclear. We study lightweight steering vectors inserted into the base model's residual stream…
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…
ESSA: Evolutionary Strategies for Scalable Alignment
Daria Korotyshova, Boris Shaposhnikov, Alexey Malakhov +7
Online alignment of large language models (LLMs) is dominated by reinforcement learning from human feedback (RLHF) with gradient-based optimizers such as PPO or GRPO. While effecti…
Steering LLM Reasoning Through Bias-Only Adaptation
Viacheslav Sinii, Alexey Gorbatovski, Artem Cherepanov +3
We show that training a single -dimensional steering vector per layer with reinforcement learning, while freezing all base weights, matches the accuracy of fully RL-tuned reason…
You Do Not Fully Utilize Transformer's Representation Capacity
Gleb Gerasimov, Yaroslav Aksenov, Nikita Balagansky +2
In contrast to RNNs, which compress their history into a single hidden state, Transformers can attend to all past tokens directly. However, standard Transformers rely solely on the…
The Differences Between Direct Alignment Algorithms are a Blur
Alexey Gorbatovski, Boris Shaposhnikov, Viacheslav Sinii +2
Direct Alignment Algorithms (DAAs) simplify LLM alignment by directly optimizing policies, bypassing reward modeling and RL. While DAAs differ in their use of SFT (one-stage vs. tw…
XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning
Alexander Nikulin, Ilya Zisman, Alexey Zemtsov +1
Following the success of the in-context learning paradigm in large-scale language and computer vision models, the recently emerging field of in-context reinforcement learning is ex…
In-Context Reinforcement Learning for Variable Action Spaces
Viacheslav Sinii, Alexander Nikulin, Vladislav Kurenkov +2
Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context. A key limit…
Emergence of In-Context Reinforcement Learning from Noise Distillation
Ilya Zisman, Vladislav Kurenkov, Alexander Nikulin +2
Recently, extensive studies in Reinforcement Learning have been carried out on the ability of transformers to adapt in-context to various environments and tasks. Current in-context…
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman +3
Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinfor…