Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909062423117824 |
|---|---|
| author | Liu, Xiaoqian Jiao, Jianbin Zhang, Junge |
| author_facet | Liu, Xiaoqian Jiao, Jianbin Zhang, Junge |
| contents | Decision-making is a dynamic process requiring perception, memory, and reasoning to make choices and find optimal policies. Traditional approaches to decision-making suffer from sample efficiency and generalization, while large-scale self-supervised pretraining has enabled fast adaptation with fine-tuning or few-shot learning in language and vision. We thus argue to integrate knowledge acquired from generic large-scale self-supervised pretraining into downstream decision-making problems. We propose Pretrain-Then-Adapt pipeline and survey recent work on data collection, pretraining objectives and adaptation strategies for decision-making pretraining and downstream inference. Finally, we identify critical challenges and future directions for developing decision foundation model with the help of generic and flexible self-supervised pretraining. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00031 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges Liu, Xiaoqian Jiao, Jianbin Zhang, Junge Machine Learning Artificial Intelligence Decision-making is a dynamic process requiring perception, memory, and reasoning to make choices and find optimal policies. Traditional approaches to decision-making suffer from sample efficiency and generalization, while large-scale self-supervised pretraining has enabled fast adaptation with fine-tuning or few-shot learning in language and vision. We thus argue to integrate knowledge acquired from generic large-scale self-supervised pretraining into downstream decision-making problems. We propose Pretrain-Then-Adapt pipeline and survey recent work on data collection, pretraining objectives and adaptation strategies for decision-making pretraining and downstream inference. Finally, we identify critical challenges and future directions for developing decision foundation model with the help of generic and flexible self-supervised pretraining. |
| title | Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.00031 |