Newest papers

Knowing When to Stop Thinking: A Dissection of Expected Free Energy as a Test-Time Compute Controller for Latent World-Model Planning

Tong Zhang, Zhiyuan Shi, Yun Peng, Tao Xie · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28011 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

学習世界モデルを使用した計画者は、予測された結果を改善し、より悪いアクションを選択することができます。予想された無料エネルギー(EFE)が計画に費やす計算量を決定するのに役立つ場合を研究します。 EFEにインスピレーションされたオブジェクトは、ゴール距離とストーカスティック予測の間の不一致を組み合わせます。我々は、候補のスコア、停止、およびオンライン予測機能の更新を別々にテストします。エリートコストストストストロールを停止すると、目標に達するために必要なエリートデータを省略すると、成功プラットフォームが深さで、固定深さの参照に近い平均成功を回復します。より大きなテスト重量で、不一致を報酬すると、凍結モデルで成功

Machine translation (Meta M2M100); the English original follows.

Abstract

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.

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Topics: Task (project management), Computer science, Computation, Exploit, Outcome (game theory), Energy (signal processing)

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