so CPC was able to somewhat be competitive or match those numbers but not not really that good finally it was also applied in reinforcement learning so in reinforcement learning you can think of accelerating your data efficiency by allowing your model or agent to learn way faster by performing these unsupervised auxilary tasks in parallel along with your reward optimization and the authors try to use contrast of losses as the auxiliary losses and they were able to see some gains on sample efficiency will not