Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for Counselor Reflection Generation
This addresses the challenge of balancing multiple rewards in reinforcement learning for a specific domain (counselor reflection generation), with incremental improvements over existing bandit methods.
The paper tackles the problem of optimizing multiple text qualities in natural language generation for counselor reflection generation, introducing two bandit methods (DynaOpt and C-DynaOpt) that dynamically adjust reward weights during training, and shows they outperform existing baselines in evaluations.
In this paper, we study the problem of multi-reward reinforcement learning to jointly optimize for multiple text qualities for natural language generation. We focus on the task of counselor reflection generation, where we optimize the generators to simultaneously improve the fluency, coherence, and reflection quality of generated counselor responses. We introduce two novel bandit methods, DynaOpt and C-DynaOpt, which rely on the broad strategy of combining rewards into a single value and optimizing them simultaneously. Specifically, we employ non-contextual and contextual multi-arm bandits to dynamically adjust multiple reward weights during training. Through automatic and manual evaluations, we show that our proposed techniques, DynaOpt and C-DynaOpt, outperform existing naive and bandit baselines, showcasing their potential for enhancing language models.