Сравнение методов

	Многоязычное обучение с подкреплением	Мультиязычный трансформер
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2010s (applied to multilingual NLP settings)	2019–2020
Автор метода	Sutton, R. S. & Barto, A. G. (RL foundations); multilingual extensions emerged from the NLP/RL community in the 2010s	Devlin et al. (mBERT); Conneau et al. (XLM-R)
Тип	Reinforcement learning applied to multilingual environments	Pre-trained cross-lingual language model
Основополагающий источник	Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press. ISBN: 978-0262193986	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186. Association for Computational Linguistics.
Другие названия	Cross-Lingual RL, Multilingual RL, Multilingual Policy Learning, Cross-Lingual Reinforcement Learning	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model
Связанные	5	4
Сводка	Multilingual Reinforcement Learning applies the RL paradigm — an agent learning by interaction and reward — to environments that involve multiple languages. The agent must interpret multilingual observations, follow cross-lingual instructions, or generalize policies trained in one language to new target languages, making it applicable to cross-lingual dialogue, multilingual game-playing agents, and language-grounded sequential decision tasks.	A multilingual transformer is a pre-trained language model built on the transformer architecture and trained jointly on text from dozens to over one hundred languages. Models such as mBERT and XLM-RoBERTa learn shared cross-lingual representations, enabling zero-shot or few-shot transfer: a model fine-tuned on English data can often be applied directly to French, German, Arabic, or Chinese without language-specific labels.