Summary of Bootstrapping Language-guided Navigation Learning with Self-refining Data Flywheel, by Zun Wang et al.
Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel
by Zun Wang, Jialu Li, Yicong Hong, Songze Li, Kunchang Li, Shoubin Yu, Yi Wang, Yu Qiao, Yali Wang, Mohit Bansal, Limin Wang
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces the Self-Refining Data Flywheel (SRDF) framework for generating high-quality navigational instruction-trajectory pairs. SRDF iteratively refines the data pool through collaboration between an instruction generator and navigator models, without human annotation. The flywheel process yields a continuously improved dataset for large-scale language-guided navigation learning. Experimental results show that after several rounds, the navigator surpasses human performance on the R2R test set, with superior performance in various downstream navigation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make better data for training language-guided agents that can navigate and understand instructions. They use a special process called Self-Refining Data Flywheel (SRDF) to improve their data without needing human help. The process starts with some initial data, then uses the trained agent to filter and improve the data, creating even better data for the next round. This keeps happening until they get really good results. They tested it and found that it worked well, even beating human performance in some cases. |
