Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales

Ju-Seung Byun, Andrew Perrault

Keywords: Reinforcement Learning, Robust Reinforcement Learning, Reverse Cross Entory

Abstract: Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance. Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) introduce additional challenges. For instance, diverse preferences complicate the alignment process, and prediction errors in a trained reward model can become more severe as the LLM generates unseen outputs. These RL challenges create confusion about whether the probability of an action for a given state should be increased or decreased, similar to the noise in labels for classification tasks. In this work, we enhance the stability of the RL training procedure by adapting reverse cross-entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss. We demonstrate performance improvements across various tasks and scales. We conduct experiments in discrete action tasks (Atari games) and continuous action space tasks (MuJoCo benchmark and Box2D) using Symmetric A2C (SA2C) and Symmetric PPO (SPPO), with and without added noise. Notably, SPPO shows strong performance across different hyperparameters. Furthermore, we validate the benefits of the symmetric RL loss in the RLHF framework using PPO for natural language processing tasks, demonstrating improved performance in tasks such as IMDB positive sentiment and TL;DR summarization.

Primary Area: reinforcement learning

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Submission Number: 4944