The Surprising Difficulty of Search in Model-Based Reinforcement Learning

• URL: http://arxiv.org/abs/2601.21306v1
• Date: Thu, 29 Jan 2026 05:58:24 GMT
• Title: The Surprising Difficulty of Search in Model-Based Reinforcement Learning
• Authors: Wei-Di Chang, Mikael Henaff, Brandon Amos, Gregory Dudek, Scott Fujimoto,
• Abstract summary: We show that search can harm performance even when the model is highly accurate.<n>We identify key techniques for enabling effective search, achieving state-of-the-art performance.
• Score: 22.08928259862334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compounding errors are the primary obstacles for model-based RL. We challenge this view, showing that search is not a plug-and-play replacement for a learned policy. Surprisingly, we find that search can harm performance even when the model is highly accurate. Instead, we show that mitigating distribution shift matters more than improving model or value function accuracy. Building on this insight, we identify key techniques for enabling effective search, achieving state-of-the-art performance across multiple popular benchmark domains.

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