4 papers
Expected Return Causes Outcome-Level Mode Collapse in Reinforcement Learning and How to Fix It with Inverse Probability Scaling
Abhijeet Sinha, Sundari Elango, Dianbo Liu
Many reinforcement learning (RL) problems admit multiple terminal solutions of comparable quality, where the goal is not to identify a single optimum but to represent a diverse set…
Deconstructing Generative Diversity: An Information Bottleneck Analysis of Discrete Latent Generative Models
Yudi Wu, Wenhao Zhao, Dianbo Liu
Generative diversity varies significantly across discrete latent generative models such as AR, MIM, and Diffusion. We propose a diagnostic framework, grounded in Information Bottle…
CodeUnlearn: Amortized Zero-Shot Machine Unlearning in Language Models Using Discrete Concept
YuXuan Wu, Bonaventure F. P. Dossou, Dianbo Liu
Large Language Models (LLMs) offer extensive knowledge across various domains, but they may inadvertently memorize sensitive, unauthorized, or malicious data, such as personal info…
Brain-inspired continual pre-trained learner via silent synaptic consolidation
Xuming Ran, Juntao Yao, Yusong Wang +2
Pre-trained models have demonstrated impressive generalization capabilities, yet they remain vulnerable to catastrophic forgetting when incrementally trained on new tasks. Existing…