Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Alami, Réda"'
Autor:
Firdoussi, Aymane El, Seddik, Mohamed El Amine, Hayou, Soufiane, Alami, Reda, Alzubaidi, Ahmed, Hacid, Hakim
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 potential solution is data pruning, which retains only high-quality data
Externí odkaz:
http://arxiv.org/abs/2410.08942
Autor:
Alami, Reda, Almansoori, Ali Khalifa, Alzubaidi, Ahmed, Seddik, Mohamed El Amine, Farooq, Mugariya, Hacid, Hakim
We demonstrate that preference optimization methods can effectively enhance LLM safety. Applying various alignment techniques to the Falcon 11B model using safety datasets, we achieve a significant boost in global safety score (from $57.64\%$ to $99.
Externí odkaz:
http://arxiv.org/abs/2409.07772
Autor:
Malartic, Quentin, Chowdhury, Nilabhra Roy, Cojocaru, Ruxandra, Farooq, Mugariya, Campesan, Giulia, Djilali, Yasser Abdelaziz Dahou, Narayan, Sanath, Singh, Ankit, Velikanov, Maksim, Boussaha, Basma El Amel, Al-Yafeai, Mohammed, Alobeidli, Hamza, Qadi, Leen Al, Seddik, Mohamed El Amine, Fedyanin, Kirill, Alami, Reda, Hacid, Hakim
We introduce Falcon2-11B, a foundation model trained on over five trillion tokens, and its multimodal counterpart, Falcon2-11B-vlm, which is a vision-to-text model. We report our findings during the training of the Falcon2-11B which follows a multi-s
Externí odkaz:
http://arxiv.org/abs/2407.14885
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 (RLHF) and direct preference optimization (DPO) require to collect cost
Externí odkaz:
http://arxiv.org/abs/2404.04291
Autor:
Mangold, Paul, Samsonov, Sergey, Labbi, Safwan, Levin, Ilya, Alami, Reda, Naumov, Alexey, Moulines, Eric
In this paper, we analyze the sample and communication complexity of the federated linear stochastic approximation (FedLSA) algorithm. We explicitly quantify the effects of local training with agent heterogeneity. We show that the communication compl
Externí odkaz:
http://arxiv.org/abs/2402.04114
In a typical stochastic multi-armed bandit problem, the objective is often to maximize the expected sum of rewards over some time horizon $T$. While the choice of a strategy that accomplishes that is optimal with no additional information, it is no l
Externí odkaz:
http://arxiv.org/abs/2310.19821
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, the automotive sector can also leverage this concept by building redunda
Externí odkaz:
http://arxiv.org/abs/2310.03767
Autor:
Chafii, Marwa, Naoumi, Salmane, Alami, Reda, Almazrouei, Ebtesam, Bennis, Mehdi, Debbah, Merouane
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 networks mandate exchanging high dimensional data in dynamic and uncerta
Externí odkaz:
http://arxiv.org/abs/2309.06021
Autor:
Achab, Mastane, Alami, Reda, Djilali, Yasser Abdelaziz Dahou, Fedyanin, Kirill, Moulines, Eric
Reinforcement learning (RL) allows an agent interacting sequentially with an environment to maximize its long-term expected return. In the distributional RL (DistrRL) paradigm, the agent goes beyond the limit of the expected value, to capture the und
Externí odkaz:
http://arxiv.org/abs/2304.14421
Autor:
Algumaei, Talal, Solozabal, Ruben, Alami, Reda, Hacid, Hakim, Debbah, Merouane, Takac, Martin
This work studies non-cooperative Multi-Agent Reinforcement Learning (MARL) where multiple agents interact in the same environment and whose goal is to maximize the individual returns. Challenges arise when scaling up the number of agents due to the
Externí odkaz:
http://arxiv.org/abs/2304.01547