1 citations · 1 across the 5 of their papers we have counts for
6 papers
Progressive Misalignment Can Emerge from Benign Recursive Self-Distillation
Karim Abdel Sadek, Federico Di Gennaro, Fanny Yang
Training models on their own outputs has attracted widespread attention as a possible route to advance AI capabilities. One prominent approach is self-distillation, where a model i…
Learning the Preferences of a Learning Agent
Karim Abdel Sadek, Mark Bedaywi, Rhys Gould +1
For AI systems to be useful to humans, they must understand and act in accordance with our values and preferences. Since specifying preferences is a hard task, inverse reinforcemen…
Mitigating Goal Misgeneralization via Minimax Regret
Karim Abdel Sadek, Matthew Farrugia-Roberts, Usman Anwar +4
Safe generalization in reinforcement learning requires not only that a learned policy acts capably in new situations, but also that it uses its capabilities towards the pursuit of…
'Explaining RL Decisions with Trajectories': A Reproducibility Study
Karim Abdel Sadek, Matteo Nulli, Joan Velja +1
This work investigates the reproducibility of the paper 'Explaining RL decisions with trajectories'. The original paper introduces a novel approach in explainable reinforcement lea…
Dynamic Vocabulary Pruning in Early-Exit LLMs
Jort Vincenti, Karim Abdel Sadek, Joan Velja +2
Increasing the size of large language models (LLMs) has been shown to lead to better performance. However, this comes at the cost of slower and more expensive inference. Early-exit…
Algorithms for Caching and MTS with reduced number of predictions
Karim Abdel Sadek, Marek Elias
ML-augmented algorithms utilize predictions to achieve performance beyond their worst-case bounds. Producing these predictions might be a costly operation -- this motivated Im et a…