3 papers
cs.LG2025
Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards
Charles Arnal, Gaëtan Narozniak, Vivien Cabannes +3
Reinforcement learning (RL) is increasingly used to align large language models (LLMs). Off-policy methods offer greater implementation simplicity and data efficiency than on-polic…
cs.CL2025
Unveiling Simplicities of Attention: Adaptive Long-Context Head Identification
Konstantin Donhauser, Charles Arnal, Mohammad Pezeshki +3
The ability to process long contexts is crucial for many natural language processing tasks, yet it remains a significant challenge. While substantial progress has been made in enha…
cs.LG2025
Easing Optimization Paths: a Circuit Perspective
Ambroise Odonnat, Wassim Bouaziz, Vivien Cabannes
Gradient descent is the method of choice for training large artificial intelligence systems. As these systems become larger, a better understanding of the mechanisms behind gradien…