The Zero-shot World Model (ZWM) significantly improves AI data efficiency, enabling visual competence with orders of magnitude less data than current state-of-the-art models. Trained on a single child's visual experience, BabyZWM matches top models on diverse visual-cognitive tasks without task-specific training, advancing a path toward more efficient AI systems.
![Zero-shot World Models Are Developmentally Efficient Learners [R]](/cdn-cgi/image/width=3840,quality=75,format=webp/https://preview.redd.it/px240r8jkuvg1.png?width=640&crop=smart&auto=webp&s=170ec87b0674ce319a19041e12e9b8cd8ff31953)
The paper proposes casting multi-label legal annotation as a retrieval task, using frozen models and k-nearest neighbors to assign labels. This method achieves competitive accuracy and strong data efficiency across legal datasets, significantly reducing computational costs compared to fine-tuning large language models.
This research introduces EasyRL, a novel data-efficient reinforcement learning approach for self-evolving LLMs, designed to overcome high annotation costs and performance issues in existing methods. Inspired by cognitive learning theory, EasyRL integrates knowledge transfer from easy labeled data with a progressive divide-and-conquer strategy for difficult unlabeled data.
This work introduces a token reweighting loss function to enhance data efficiency in training vision-language models for medical report generation. By prioritizing semantically salient tokens, the method achieves comparable report quality using up to ten times less training data.