AIConfPaper
← Search

Cristina Pinneri

5 accepted papers

2026

Masks Can Be Distracting: On Context Comprehension in Diffusion Language Models

Julianna Piskorz, Cristina Pinneri, Alvaro Correia, Motasem Alfarra, Risheek Garrepalli, Christos Louizos
ICML 2026poster

Masked Diffusion Language Models (MDLMs) have recently emerged as a promising alternative to Autoregressive Language Models (ARLMs), leveraging a denoising objective that, in principle, should enable more uniform context utilisation. In this work, we examine the context comprehension abilities of MD…

Cited by 0Source ↗Scholar ↗
2023

Pink Noise Is All You Need: Colored Noise Exploration in Deep Reinforcement Learning

Onno Eberhard, Jakob Hollenstein, Cristina Pinneri, Georg Martius
ICLR 2023top-25%

In off-policy deep reinforcement learning with continuous action spaces, exploration is often implemented by injecting action noise into the action selection process. Popular algorithms based on stochastic policies, such as SAC or MPO, inject white noise by sampling actions from uncorrelated Gaussia…

Cited by 51Source ↗PDF ↗Scholar ↗
2021

Extracting Strong Policies for Robotics Tasks from Zero-Order Trajectory Optimizers

Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, Georg Martius
ICLR 2021poster

Solving high-dimensional, continuous robotic tasks is a challenging optimization problem. Model-based methods that rely on zero-order optimizers like the cross-entropy method (CEM) have so far shown strong performance and are considered state-of-the-art in the model-based reinforcement learning comm…

Cited by 13Source ↗PDF ↗Scholar ↗
2020

Sample-efficient Cross-Entropy Method for Real-time Planning

Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, Jan Achterhold, Joerg Stueckler, Michal Rolinek +1
CoRL 2020

Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse-reward environments. However, their sampling inefficiency prevents them from being used for real-time planning and cont

Cited by 156Source ↗PDF ↗code ↗Scholar ↗