Papers with pretrained policy
Learning Natural Language Generation with Truncated Reinforcement Learning (2022.naacl-main)
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| Challenge: | Existing approaches to train conditional languagemodels without supervised learning fail to scale to large action spaces, thus allowing to train a language agent by only interacting with its environment without any task-specific prior knowledge. |
| Approach: | They propose an original approach to train conditional languagemodels without supervised learning by only using reinforcement learning. |
| Outcome: | The proposed approach avoids the dependency to labelled datasets and reduces pretrained policy flaws such as language or exposure biases. |
Learning How to Active Learn by Dreaming (P19-1)
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| Challenge: | Recent active learning methods are limited when the data distribution of learning problems vary. |
| Approach: | They propose a wake-and-dream-based active learning method that learns the AL policy directly on the target domain of interest by using wake and dream cycles. |
| Outcome: | The proposed method improves on cross-domain and cross-lingual tasks. |