研究指令粒度对智能体行为的影响，发现精细与粗略指令效果最好。
Mini-BEHAVIOR-Gran: Revealing U-Shaped Effects of Instruction Granularity on Language-Guided Embodied Agents
- 构建多粒度指令基准Mini-BEHAVIOR-Gran，支持同一任务不同描述层次测试。
- 发现指令粒度与性能呈U型关系，极细和极粗指令下表现最优。
- 粗粒度优势源于视觉主导策略，适合研究视觉感知强化的场景。
指令粒度是语言引导具身智能领域中一个重要但控制不足的变量。现有基准通常为每个任务配置单一固定指令，难以研究相同任务在不同描述细节下的行为变化。本文提出Mini-BEHAVIOR-Gran，作为Mini-BEHAVIOR的扩展，为每个任务提供从高层目标到分步指导的多种指令变体。通过该基准，我们对比了四种跨任务粒度量化指标：词元数量、实体数、动作动词数与规划宽度，发现规划宽度与智能体性能相关性最强。进一步以宽度组织训练与评估，揭示出指令粒度与性能之间非单调的U型关系，在精细与粗略两个极端均出现性能峰值。深入分析表明，粗粒度下的性能回升与浅层语义接地有关，即智能体学习到以视觉为主的策略。
原文摘要 · Abstract (English)
Instruction granularity is an important yet poorly controlled variable in language-guided embodied AI. Existing benchmarks typically pair each task with a single static instruction, making it difficult to study how agent behavior changes when the same task is described at different levels of detail. We introduce Mini-BEHAVIOR-Gran, a new benchmark for controlled studies of instruction granularity that extends Mini-BEHAVIOR with multiple instruction variants per task, ranging from high-level goal descriptions to step-by-step guidance. Using this benchmark, we compare four candidate metrics for cross-task granularity quantification: token count, entity count, action-verb count, and planning-width, and find that width correlates most consistently with agent performance. Using width to organize training and evaluation further reveals a non-monotonic U-shaped relationship between instruction granularity and performance, with peaks at both fine and coarse extremes. Further analysis suggests that the coarse-granularity performance rebound is associated with shallow grounding, where agents learn vision-dominant policies.
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