1 citations · 1 across the 9 of their papers we have counts for
11 papers
WISE: World-model-guided Imagination Scheduling for Efficient Post-training of Vision-Language-Action Models
Chenhao Zhang, Hanyu Zhao, Hang Cheng +2
Post-training VLA policies typically rely on supervised fine-tuning with costly expert demonstrations or reinforcement learning with expensive and potentially unstable real-world e…
DataCube: A Video Retrieval Platform via Natural Language Semantic Profiling
Yiming Ju, Hanyu Zhao, Quanyue Ma +5
Large-scale video repositories are increasingly available for modern video understanding and generation tasks. However, transforming raw videos into high-quality, task-specific dat…
Accelerate Scaling of LLM Finetuning via Quantifying the Coverage and Depth of Instruction Set
Chengwei Wu, Li Du, Hanyu Zhao +4
Scaling the amount of data used for supervied fine-tuning(SFT) does not guarantee the proportional gains in model performance, highlighting a critical need to understand what makes…
CI-VID: A Coherent Interleaved Text-Video Dataset
Yiming Ju, Jijin Hu, Zhengxiong Luo +7
Text-to-video (T2V) generation has recently attracted considerable attention, resulting in the development of numerous high-quality datasets that have propelled progress in this ar…
Scaling Towards the Information Boundary of Instruction Sets: The Infinity Instruct Subject Technical Report
Li Du, Hanyu Zhao, Yiming Ju +1
Instruction tuning has become a foundation for unlocking the capabilities of large-scale pretrained models and improving their performance on complex tasks. Thus, the construction…
Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models
Jijie Li, Li Du, Hanyu Zhao +5
Large Language Models (LLMs) demonstrate strong performance in real-world applications, yet existing open-source instruction datasets often concentrate on narrow domains, such as m…