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cs.AI2024
'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…
cs.CL2024
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…
cs.CL2024
Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs
Yohan Mathew, Ollie Matthews, Robert McCarthy +4
The rapid proliferation of frontier model agents promises significant societal advances but also raises concerns about systemic risks arising from unsafe interactions. Collusion to…