Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., and Diaz-Rodriguez, N. (2020). Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges. Information fusion, 58:52-68.
Lester, B., Al-Rfou, R., and Constant, N. (2021). The power of scale for parameter-efficient prompt tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3045-3059, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Li, W., Luo, H., Lin, Z., Zhang, C., Lu, Z., and Ye, D. (2023). A survey on transformers in reinforcement learning. arXiv preprint arXiv:2301.03044.
Li, X. L. and Liang, P. (2021). Prefix-tuning: Optimizing continuous prompts for generation. In Zong, C., Xia, F., Li, W., and Navigli, R., editors, Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, pages 4582-4597. Association for Computational Linguistics.
Liu, F., Liu, H., Grover, A., and Abbeel, P. (2022a). Masked autoencoding for scalable and generalizable decision making. Advances in Neural Information Processing Systems, 35:12608-12618.
Liu, H. and Abbeel, P. (2023). Emergent agentic transformer from chain of hindsight experience. arXiv preprint arXiv:2305.16554.
Liu, H., Tam, D., Muqeeth, M., Mohta, J., Huang, T., Bansal, M., and Raffel, C. (2022b). Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. arXiv preprint arXiv:2205.05638.
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G. (2021a). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586.
Liu, X., Ji, K., Fu, Y., Du, Z., Yang, Z., and Tang, J. (2021b). P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks. arXiv preprint arXiv:2110.07602.
Lopez-Paz, D. and Ranzato, M. (2017). Gradient episodic memory for continual learning. Advances in neural information processing systems, 30.
Loshchilov, I. and Hutter, F. (2018). Decoupled weight decay regularization. In International Conference on Learning Representations.
Maas, A. L., Hannun, A. Y., Ng, A. Y., et al. (2013). Rectifier nonlinearities improve neural network acoustic models. In Proc. icml, volume 30, page 3. Atlanta, Georgia, USA.
Mallya, A. and Lazebnik, S. (2018). Packnet: Adding multiple tasks to a single network by iterative pruning. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 7765-7773.
Mandi, Z., Abbeel, P., and James, S. (2022). On the effectiveness of fine-tuning versus meta-reinforcement learning. arXiv preprint arXiv:2206.03271.
Mao, Y., Mathias, L., Hou, R., Almahairi, A., Ma, H., Han, J., Yih, S., and Khabsa, M. (2022). Unipelt: A unified framework for parameter-efficient language model tuning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6253-6264.
McCloskey, M. and Cohen, N. (1989). Catastrophic interference in connectionist networks: The sequential learning problem. In *Psychology of learning and motivation*, volume 24, pages 109-165. Elsevier.
Meng, L., Wen, M., Yang, Y., Le, C., Li, X., Zhang, W., Wen, Y., Zhang, H., Wang, J., and Xu, B. (2021). Offline pre-trained multi-agent decision transformer: One big sequence model conquers all starcraftii tasks. arXiv preprint arXiv:2112.02845.