PACE: 時間効率的な具現化計画のための適応予算配分
PACE: Adaptive Budget Allocation for Time-Efficient Embodied Planning
推論と実行をインターリーブし、実行時間に合わせて推論トークン予算を動的に調整することで、具現化計画の推論遅延を大幅に削減するフレームワークを提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Yuchen Huang, Xijiang Ying, Zhenhua Ma, Xiaxiang Yuan, Zhijie Gao, Jiayi Huang, Ruichi Mao, Jiazheng Zhang, Hongsheng Ti, Maotao Tian, Rong Shi, Lu Zhao, Shizhuang Zhang, Zhuo Cui, He Wang, Ling Liu, Wei Zhang
分類: cs.RO, cs.AI
原文アブストラクト
Reasoning-enhanced large language models have achieved remarkable improvements in planning tasks, yet their deployment in embodied systems remains impractical due to prohibitive inference delays-often exceeding minutes per planning instance. The fundamental bottleneck stems from the serial nature of existing paradigms: models must complete all reasoning before any action execution, leaving execution time windows entirely unexploited. We introduce PACE (Planning with Adaptive Cognitive Effort), a framework that enables interleaved reasoning and execution through two key innovations: an Interleaved Think-Act architecture that pipelines cognitive processing with action execution, and a Dynamic Budget Allocator that adapts reasoning token budgets to available execution time windows. On the Robotouille benchmark using Qwen3-8B-AWQ, PACE achieves a 10% success rate-representing a 67% improvement over the ReAct+Think baseline-while delivering 6.9 times acceleration in thinking time compared to unconstrained reasoning. The framework hides 66.8% of thinking time within execution windows, demonstrating that strategic cognitive effort allocation can simultaneously improve both planning quality and time efficiency. These results provide evidence that time-aware architectural innovations enable reasoning models to operate in latency-sensitive embodied domains where they were previously impractical.
