Semi-Offline Reinforcement Learning for Optimized Text Generation

TL;DR

This paper introduces semi-offline reinforcement learning, a new paradigm that balances exploration and training cost, with theoretical foundations and empirical results showing improved efficiency and performance in text generation tasks.

Contribution

It proposes a semi-offline RL framework that bridges offline and online learning, providing theoretical analysis and demonstrating superior efficiency and effectiveness.

Findings

Semi-offline RL reduces training costs compared to online methods.

The approach achieves comparable or better performance than state-of-the-art methods.

Theoretical analysis supports the optimality of the semi-offline setting.

Abstract

In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline. Online methods explore the environment at significant time cost, and offline methods efficiently obtain reward signals by sacrificing exploration capability. We propose semi-offline RL, a novel paradigm that smoothly transits from offline to online settings, balances exploration capability and training cost, and provides a theoretical foundation for comparing different RL settings. Based on the semi-offline formulation, we present the RL setting that is optimal in terms of optimization cost, asymptotic error, and overfitting error bound. Extensive experiments show that our semi-offline approach is efficient and yields comparable or often better performance compared with state-of-the-art methods.