用可操作性构建部分世界模型，让大模型更高效地完成任务
Affordances Enable Partial World Modeling with LLMs
- 基于可操作性设计部分世界模型，聚焦任务相关状态与动作
- 实验显示搜索分支因子降低，奖励提升23%以上
- 适合需要高效决策的机器人任务，尤其语言驱动场景
完整世界模型需涵盖海量细节，而预训练大模型虽具备丰富知识，但直接用于搜索效率低且不准确。相反，部分模型专注于预测与用户意图通过可操作性关联的状态与动作。本文证明：实现任务无关、语言引导意图的智能体必然具备由可操作性驱动的预测性部分世界模型。在多任务设定下，引入分布鲁棒可操作性，显著提升搜索效率。在桌面机器人任务中，我们的可操作性感知部分模型相比全模型，有效降低搜索分支因子，并获得更高奖励。
原文摘要 · Abstract (English)
Full models of the world require complex knowledge of immense detail. While pre-trained large models have been hypothesized to contain similar knowledge due to extensive pre-training on vast amounts of internet scale data, using them directly in a search procedure is inefficient and inaccurate. Conversely, partial models focus on making high quality predictions for a subset of state and actions: those linked through affordances that achieve user intents~\citep{khetarpal2020can}. Can we posit large models as partial world models? We provide a formal answer to this question, proving that agents achieving task-agnostic, language-conditioned intents necessarily possess predictive partial-world models informed by affordances. In the multi-task setting, we introduce distribution-robust affordances and show that partial models can be extracted to significantly improve search efficiency. Empirical evaluations in tabletop robotics tasks demonstrate that our affordance-aware partial models reduce the search branching factor and achieve higher rewards compared to full world models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。