外观
论文arXiv 2610.2224513 天前
分歧引导的弱到强路由
原文链接：Disagreement-Guided Weak-to-Strong Routing（arXiv 2610.22245 · 发布日期：2026-10-15）
Disagreement-Guided Weak-to-Strong Routing
针对弱模型部署中何时调用强模型的问题，提出基于模型分歧的无监督路由，并在智能体式和单轮任务上验证。
核心要点
- 小语言模型适合边缘和机器人场景的成本与延迟约束，但能力有限影响可靠部署。
- 方法刻画允许多个弱模型本地响应的最优路由，并用模型间比较进行原则性近似。
- 提出可由模型生成响应直接测量的模型分歧，无需真实正确性标签监督。
- 在智能体式和单轮任务中，该方法有效权衡路由成本与响应准确率。
验证范围与局限：验证范围为智能体式和单轮任务，摘要未说明具体系统或任务细节。
英文摘要 (Abstract)
Small language models are increasingly deployed on edge devices and robotic systems to meet cost and latency constraints, yet their limited capabilities can make reliable deployment challenging. Stronger models can provide assistance when needed, but deciding when to route a weak-model response to a stronger model often relies on signals of response correctness. In open-ended and agentic tasks, however, obtaining such signals can require costly ground-truth supervision that is difficult to obtain at scale. We revisit the foundations of weak-to-strong routing and ask whether routing decisions can instead be guided by signals that do not require ground-truth supervision. We first characterize the optimal weak-to-strong routing policy, while allowing for multiple weak-model responses generated through local inference. Building on this characterization, we derive a principled approximation to the optimal routing based on model-to-model comparison. In particular, we introduce a notion of model disagreement that can be measured directly from model-generated responses, without ground-truth supervision. Importantly, the approximation becomes increasingly accurate as the strong model becomes a better proxy for the ground-truth distribution. Across agentic and single-turn tasks, our approach effectively navigates the trade-off between routing cost and response accuracy without ground-truth supervision. Moreover, even when ground-truth correctness signals are available for training, disagreement-based routing can outperform methods trained directly to predict correctness
作者：Sima Noorani, Shayan Kiyani, George J. Pappas, Hamed Hassani
来源
本页事实对应的公开页面，以原文为准。