Summary of Self-supervised Pretraining For Decision Foundation Model: Formulation, Pipeline and Challenges, by Xiaoqian Liu et al.
Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges
by Xiaoqian Liu, Jianbin Jiao, Junge Zhang
First submitted to arxiv on: 29 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| 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 proposed Pretrain-Then-Adapt pipeline integrates knowledge acquired from large-scale self-supervised pretraining into downstream decision-making problems. Traditional approaches suffer from sample efficiency and generalization, but this approach enables fast adaptation with fine-tuning or few-shot learning. The paper surveys recent work on data collection, pretraining objectives, and adaptation strategies for decision-making pretraining and downstream inference. The authors identify critical challenges and future directions for developing a decision foundation model using generic and flexible self-supervised pretraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of researchers wants to make it easier to make good decisions by combining what they’ve learned from big datasets with special training methods. They think this will help solve problems where we need to make choices based on memory, reasoning, and perception. The approach they suggest is called Pretrain-Then-Adapt, and it involves using large-scale self-supervised pretraining for decision-making tasks. The paper looks at what others have done in this area and talks about the challenges and next steps. |
Keywords
* Artificial intelligence * Few shot * Fine tuning * Generalization * Inference * Pretraining * Self supervised
