Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks

Understand

Large volumes of interaction logs can be collected from NLP systems that are deployed in the real world.

• How can this wealth of information be leveraged? Using such interaction logs in an offline reinforcement learning (RL) setting is a promising approach.
• However, due to the nature of NLP tasks and the constraints of production systems, a series of challenges arise.
• We present a concise overview of these challenges and discuss possible solutions.