3 papers
cs.LG2026
A Comparative Theoretical Analysis of Entropy Control Methods in Reinforcement Learning
Ming Lei, Christophe Baehr
Reinforcement learning (RL) has become a key approach for enhancing reasoning in large language models (LLMs), yet scalable training is often hindered by the rapid collapse of poli…
cs.LG2026
HCP-DCNet: A Hierarchical Causal Primitive Dynamic Composition Network for Self-Improving Causal Understanding
Ming Lei, Shufan Wu, Christophe Baehr
The ability to understand and reason about cause and effect -- encompassing interventions, counterfactuals, and underlying mechanisms -- is a cornerstone of robust artificial intel…
cs.LG2026
A Geometrically-Grounded Drive for MDL-Based Optimization in Deep Learning
Ming Lei, Shufan Wu, Christophe Baehr
This paper introduces a novel optimization framework that fundamentally integrates the Minimum Description Length (MDL) principle into the training dynamics of deep neural networks…