Reinforcement learning (RL), a powerful technique for training AI, is like teaching a dog new tricks with rewards and punishments. But sometimes, the training process can be unpredictable, with the AI's performance fluctuating wildly. This instability stems from several factors. Think of it like trying to hit a moving target – the optimal strategy for the AI keeps changing as it learns. Plus, there's a lot of noise in the feedback the AI receives, making it hard to distinguish good moves from bad ones. This is especially true in areas like Reinforcement Learning from Human Feedback (RLHF) or AI Feedback (RLAIF), where conflicting opinions or inaccurate reward models can further muddy the waters. Now, researchers are tackling this instability by borrowing a trick from supervised learning, the more traditional way of training AI. They've developed a "symmetric RL loss," a new way of calculating how wrong the AI is. This loss function is designed to be more resistant to noise, helping the AI learn more steadily even when the feedback is unreliable. The results? A more stable learning process across different tasks and AI model sizes. This new approach, tested on everything from video games to language models, shows promising improvements in performance and robustness. Specifically, the researchers saw significant gains when using this symmetric loss with Proximal Policy Optimization (PPO), a popular RL algorithm. This suggests that the symmetric loss is particularly effective at mitigating the noise introduced by PPO's off-policy nature and advantage normalization. While this research focuses on A2C and PPO, it opens doors for exploring similar techniques in other RL algorithms. The quest for more stable and robust RL continues, promising more reliable and predictable AI in the future.