3 citations · 3 across the 9 of their papers we have counts for
6 papers · 1 filter
Temporal Recurrence Favors Fewer Layers
Ivan Anokhin, Johan Obando-Ceron, Irina Rish +1
In streaming tasks, recurrent models can carry latent computation across time, allowing each update to build on representations produced earlier. This raises a basic question: once…
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control
Donghu Kim, Youngdo Lee, Hojoon Lee +6
Improving sample efficiency remains a core challenge in reinforcement learning (RL), especially in real-world settings like robotics, where data collection is costly. This challeng…
Local Guidance, Global Impact: Gaussian-Reshaped Trust Region Unlocks Behavior Transitions
Bingxu Liu, Jiashun Liu, Johan Obando-Ceron +5
While Proximal Policy Optimization (PPO) demonstrates strong performance in stationary settings, we show that its standard optimization paradigm struggles in continual and non-stat…
A Mechanistic Analysis of Looped Reasoning Language Models
Hugh Blayney, Álvaro Arroyo, Johan Obando-Ceron +4
Reasoning has become a central capability in large language models. Recent research has shown that reasoning performance can be improved by looping an LLM's layers in the latent di…
Asymmetric Proximal Policy Optimization: mini-critics boost LLM reasoning
Jiashun Liu, Johan Obando-Ceron, Han Lu +7
Most recent RL for LLMs (RL4LLM) methods avoid explicit critics, replacing them with average advantage baselines. This shift is largely pragmatic: conventional value functions are…
Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data
Johan S. Obando-Ceron, Victor Romero Cano, Walter Mayor Toro
This paper presents a framework for efficiently learning feature selection policies which use less features to reach a high classification precision on large unstructured data. It…