PACE: Adaptive Budget Allocation for Time-Efficient Embodied Planning

2026-08-03

AI Summary

This work addresses the high latency of large language models in embodied planning, where sequential reasoning impedes real-time performance. To overcome this limitation, the authors propose PACE, a novel framework featuring an interleaved think-execute pipeline architecture coupled with a dynamic reasoning budget allocation mechanism. This design enables parallelization of cognitive reasoning and action execution, adaptively scheduling computational resources according to execution time windows. Evaluated on the Robotouille benchmark using the Qwen3-8B-AWQ model, PACE achieves a task success rate of 10%, representing a 67% improvement over the ReAct+Think baseline, while accelerating reasoning by 6.9×. Notably, 66.8% of the reasoning time is effectively hidden within action execution windows.

Abstract

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.

Problem

• embodied planning
• inference delay
• reasoning-execution bottleneck
• time efficiency
• latency-sensitive systems

Innovation

• Interleaved Think-Act
• Dynamic Budget Allocation
• Embodied Planning
• Time-Efficient Reasoning
• Token Budget Adaptation