6 papers
Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling
Falcon LLM Team, Iheb Chaabane, Puneesh Khanna +8
This work introduces Falcon-H1R, a 7B-parameter reasoning-optimized model that establishes the feasibility of achieving competitive reasoning performance with small language models…
Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Jingwei Zuo, Maksim Velikanov, Ilyas Chahed +24
In this report, we introduce Falcon-H1, a new series of large language models (LLMs) featuring hybrid architecture designs optimized for both high performance and efficiency across…
Accurate and Diverse LLM Mathematical Reasoning via Automated PRM-Guided GFlowNets
Adam Younsi, Ahmed Attia, Abdalgader Abubaker +3
Achieving both accuracy and diverse reasoning remains challenging for Large Language Models (LLMs) in complex domains like mathematics. A key bottleneck is evaluating intermediate…
PORT: Preference Optimization on Reasoning Traces
Salem Lahlou, Abdalgader Abubaker, Hakim Hacid
Preference optimization methods have been successfully applied to improve not only the alignment of large language models (LLMs) with human values, but also specific natural langua…
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 (…
Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks
Abdalgader Abubaker, Takanori Maehara, Madhav Nimishakavi +1
Recently, pretraining methods for the Graph Neural Networks (GNNs) have been successful at learning effective representations from unlabeled graph data. However, most of these meth…