2 citations · 2 across the 11 of their papers we have counts for
6 papers · 1 filter
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…