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

TL;DR

This paper explores the potential of using large-scale real-world interaction logs in offline reinforcement learning to improve NLP systems, addressing unique challenges and proposing solutions.

Contribution

It provides an overview of challenges in applying offline RL to real-world NLP tasks and discusses potential strategies to overcome them.

Findings

Identifies key challenges in offline RL for NLP.

Proposes solutions for data collection and policy learning.

Highlights the importance of real-world interaction logs.

Abstract

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.