1 citations · 2 across the 9 of their papers we have counts for
12 papers
Tropical Reinforcement Learning
Arip Asadulaev, Aladin Djuhera, Karim Salta +3
Reinforcement learning for large language models typically maximizes expected return, adding up the probabilities of all successful trajectories. However, the classical sum formula…
Branched Optimal Transport Amortization
Semyon Semenov, Viktor Kovalchuk, Meir Roketlishvili +4
Methods of Branched Optimal Transport (BOT) mimic the economy and efficiency of natural tree-like structures, such as those found in rivers and biological systems. These methods ar…
Entropy-Gated Latent Recursion
Soham Bhattacharjee, Dushyant Singh Chauhan, Salem Lahlou +2
Inference-time scaling has become the dominant lever for improving language-model reasoning, but existing methods derive rollout diversity from a single source: stochastic token-le…
Convex Compositional Reasoning Models
Meir Roketlishvili, Semyon Semenov, Maksim Bobrin +7
Compositional energy-based models can generalize to larger combinatorial reasoning problems by reusing a learned factor energy across many local constraints. In our paper, we show…
WaveSSM: Multiscale State-Space Models for Non-stationary Signal Attention
Ruben Solozabal, Velibor Bojkovic, Hilal Alquabeh +3
State-space models (SSMs) have emerged as a powerful foundation for long-range sequence modeling, with the HiPPO framework showing that continuous-time projection operators can be…
Zero-Shot Off-Policy Learning
Arip Asadulaev, Maksim Bobrin, Salem Lahlou +3
Off-policy learning methods seek to derive an optimal policy directly from a fixed dataset of prior interactions. This objective presents significant challenges, primarily due to t…
Latent Reasoning in TRMs is Secretly a Policy Improvement Operator
Arip Asadulaev, Rayan Banerjee, Fakhri Karray +1
Recently, small models with latent recursion have obtained promising results on complex reasoning tasks. These results are typically explained by the theory that such recursion inc…
Expert or not? assessing data quality in offline reinforcement learning
Arip Asadulaev, Fakhri Karray, Martin Takac
Offline reinforcement learning (RL) learns exclusively from static datasets, without further interaction with the environment. In practice, such datasets vary widely in quality, of…
Y-Shaped Generative Flows
Arip Asadulaev, Semyon Semenov, Abduragim Shtanchaev +3
Modern continuous-time generative models typically induce \emph{V-shaped} flows: each sample travels independently along a nearly straight trajectory from the prior to the data. Al…
Uncovering the Spectral Bias in Diagonal State Space Models
Ruben Solozabal, Velibor Bojkovic, Hilal AlQuabeh +2
Current methods for initializing state space models (SSMs) parameters mainly rely on the \textit{HiPPO framework}, which is based on an online approximation of orthogonal polynomia…
The AI Data Scientist
Farkhad Akimov, Munachiso Samuel Nwadike, Zangir Iklassov +1
Imagine decision-makers uploading data and, within minutes, receiving clear, actionable insights delivered straight to their fingertips. That is the promise of the AI Data Scientis…
Enhance Hyperbolic Representation Learning via Second-order Pooling
Kun Song, Ruben Solozabal, Li hao +5
Hyperbolic representation learning is well known for its ability to capture hierarchical information. However, the distance between samples from different levels of hierarchical cl…