思考を止めるタイミングの見極め：潜在世界モデル計画におけるテスト時計算制御としての期待自由エネルギーの解剖
Knowing When to Stop Thinking: A Dissection of Expected Free Energy as a Test-Time Compute Controller for Latent World-Model Planning
学習した世界モデルを用いた計画において、期待自由エネルギー（EFE）に基づく目的関数が計算量の制御にどう役立つかを検証し、コスト停滞による停止規則の有効性と限界を分析した論文。
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著者: Tong Zhang, Zhiyuan Shi, Yun Peng, Tao Xie
分類: cs.RO
原文アブストラクト
A planner using a learned world model can improve its predicted outcome while choosing a worse action. We study when expected free energy (EFE) can help decide how much computation to spend on a plan. An EFE-inspired objective combines goal distance with disagreement among stochastic predictions. We test candidate scoring, stopping, and online predictor updates separately. Stopping when elite cost stalls reduces refinement on tasks whose success plateaus with depth, with mean success close to fixed-depth references. At the larger tested weights, rewarding disagreement lowers success with a frozen model. Penalizing it helps at some task and weight settings but harms others. When training data omit a region needed to reach the goal, online updates recover part of the resulting loss. The tested cost-stall rule can fail when further optimization exploits model errors, because predicted cost keeps falling after task success peaks. Cost progress can guide computation on a plateau, but protecting performance after a peak requires information about the consequences of the chosen actions.