2 citations · 3 across the 11 of their papers we have counts for
6 papers · 1 filter
Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory
Aymane El Firdoussi, Mohamed El Amine Seddik, Soufiane Hayou +3
Synthetic data has gained attention for training large language models, but poor-quality data can harm performance (see, e.g., Shumailov et al. (2023); Seddik et al. (2024)). A pot…
Alignment with Preference Optimization Is All You Need for LLM Safety
Reda Alami, Ali Khalifa Almansoori, Ahmed Alzubaidi +3
We demonstrate that preference optimization methods can effectively enhance LLM safety. Applying various alignment techniques to the Falcon 11B model using safety datasets, we achi…
Investigating Regularization of Self-Play Language Models
Reda Alami, Abdalgader Abubaker, Mastane Achab +2
This paper explores the effects of various forms of regularization in the context of language model alignment via self-play. While both reinforcement learning from human feedback (…
A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits
Reda Alami, Mohammed Mahfoud, Mastane Achab
In a typical stochastic multi-armed bandit problem, the objective is often to maximize the expected sum of rewards over some time horizon . While the choice of a strategy that a…
Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study
Fouzi Boukhalfa, Reda Alami, Mastane Achab +2
In today's era, autonomous vehicles demand a safety level on par with aircraft. Taking a cue from the aerospace industry, which relies on redundancy to achieve high reliability, th…
Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks
Marwa Chafii, Salmane Naoumi, Reda Alami +3
In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption. Future wireless ne…