2 citations · 3 across the 4 of their papers we have counts for
4 papers · 1 filter
LS-AR: Future-Predictive Latent Steering in Autoregressive LLMs
Anubha Gupta, Eduardo Pignatelli
Standard autoregressive (AR) models process high-level task instructions, state history, and transient tokens within a single shared sequence of tokens. Consequently, they lack the…
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL
Eduardo Pignatelli, Johan Ferret, Tim Rockäschel +4
The temporal credit assignment problem is a central challenge in Reinforcement Learning (RL), concerned with attributing the appropriate influence to each actions in a trajectory f…
NAVIX: Scaling MiniGrid Environments with JAX
Eduardo Pignatelli, Jarek Liesen, Robert Tjarko Lange +3
As Deep Reinforcement Learning (Deep RL) research moves towards solving large-scale worlds, efficient environment simulations become crucial for rapid experimentation. However, mos…
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
Eduardo Pignatelli, Johan Ferret, Matthieu Geist +4
The Credit Assignment Problem (CAP) refers to the longstanding challenge of Reinforcement Learning (RL) agents to associate actions with their long-term consequences. Solving the C…