PrefPoE: Advantage-Guided Preference Fusion for Learning Where to Explore

The PrefPoE framework, introduced on November 12, 2025, represents a breakthrough in reinforcement learning, particularly in the area of exploration. Traditional methods often struggle with high variance and inefficient policy updates due to naive entropy maximization. PrefPoE addresses these issues by employing a Preference-Product-of-Experts approach, which intelligently guides exploration based on action advantages. This method not only stabilizes policy updates but also enhances training stability and sample efficiency. The framework has demonstrated impressive performance improvements across various control tasks, including a 321% increase on HalfCheetah-v4, a 69% increase on Ant-v4, and a 276% increase on LunarLander-v2. Unlike standard PPO, which can suffer from entropy collapse, PrefPoE maintains adaptive exploration dynamics, preventing premature convergence and ensuring more effective learning. This advancement highlights the importance of learning where to explore, which is …