Traditional approaches to RL have focused on learning decision policies directly from episodic decisions, while slowly and implicitly learning the semantics of compositional representations needed for generalization. While some approaches have been adopted to refine representations via auxiliary self-supervised losses while simultaneously learning decision policies, learning compositional representations from hand-designed and context-independent self-supervised losses (multi-view) still adapts relatively slowly to the real world, which contains many non-IID subspaces requiring rapid distribution shift in both time and spatial attention patterns at varying levels of abstraction. In contrast, supervised language model cascades have shown the flexibility to adapt to many diverse manifolds, and hints of self-learning needed for autonomous task transfer. However, to date, transfer methods for language models like few-shot learning and fine-tuning still require human supervision and transfer learning using self-learning methods has been underexplored. We propose a self-supervised loss policy called contrastive distillation which manifests latent variables with high mutual information with both source and target tasks from weights to tokens. We show how this outperforms common methods of transfer learning and suggests a useful design axis of trading off compute for generalizability for online transfer. Contrastive distillation is improved through sampling from memory and suggests a simple algorithm for more efficiently sampling negative examples for contrastive losses than random sampling.

对话系统必须能够随着时间的推移通过更新来纳入新技能，以反映新的用例或部署方案。同样，此类ML驱动系统的开发人员需要能够在已经存在的数据集中添加新的培训数据，以支持这些新技能。在意图分类系统中，如果培训数据的新技能意图与已经存在的意图重叠，则可能会出现问题。我们称此类案件发生冲突。本文介绍了多个数据集之间意图碰撞检测的任务，以提高系统的技能。我们介绍了几种检测碰撞的方法，并评估我们在展示碰撞的真实数据集上的方法。为了强调对意图碰撞检测的需求，我们表明，如果添加新数据，则模型性能会受到影响。最后，我们使用碰撞检测来构建和基准一个新的数据集Redwood，该数据集由13个原始意图分类数据集中的451个Nentent类别组成，使其成为最大的公开可用意图分类基准。