3 papers
cs.LG2026
Denser Better: Limits of On-Policy Self-Distillation for Continual Post-Training
Meng Wang, Haohan Zhao, Wenzhuo Liu +7
Continual post-training enables foundation models to acquire new knowledge while preserving existing capabilities. Recent work suggests that on-policy learning can mitigate forgett…
cs.LG2026
Self-Consolidation for Self-Evolving Agents
Hongzhuo Yu, Fei Zhu, Guo-Sen Xie +1
While large language model (LLM) agents have demonstrated impressive problem-solving capabilities, they typically operate as static systems, lacking the ability to evolve through l…
cs.CV2024
Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning
Binqian Xu, Xiangbo Shu, Jiachao Zhang +2
Contrastive learning, relying on effective positive and negative sample pairs, is beneficial to learn informative skeleton representations in unsupervised skeleton-based action rec…