11 papers
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…
Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training
Song Lai, Haohan Zhao, Rong Feng +9
Continual post-training (CPT) is a popular and effective technique for adapting foundation models like multimodal large language models to ever-evolving downstream tasks. While exi…
C-NAV: Towards Self-Evolving Continual Object Navigation in Open World
Ming-Ming Yu, Fei Zhu, Wenzhuo Liu +4
Embodied agents are expected to perform object navigation in dynamic, open-world environments. However, existing approaches typically rely on static trajectories and a fixed set of…
CL-VISTA: Benchmarking Continual Learning in Video Large Language Models
Haiyang Guo, Yichen Shi, Fei Zhu +6
Video Large Language Models (Video-LLMs) require continual learning to adapt to non-stationary real-world data. However, existing benchmarks fall short of evaluating modern foundat…
MCITlib: Multimodal Continual Instruction Tuning Library and Benchmark
Haiyang Guo, Fei Zhu, Hongbo Zhao +5
Continual learning enables AI systems to acquire new knowledge while retaining previously learned information. While traditional unimodal methods have made progress, the rise of Mu…
VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?
Hongbo Zhao, Meng Wang, Fei Zhu +5
The computational and memory overheads associated with expanding the context window of LLMs severely limit their scalability. A noteworthy solution is vision-text compression (VTC)…