想减少长链推理的延迟？LASER用负载感知早退，在边缘设备上平衡速度和准确率，值得一试！
DeepSeek-R1等大型推理模型通过长链思维（CoT）获得强性能，但边缘设备资源受限。现有基于置信度的早退方法从单请求角度固定阈值，忽略多请求并发和负载波动。提出LASER，包含负载感知自适应退出阈值和难度-负载感知推理预算预分配。在2个推理模型、4个基准、不同负载条件下，平均延迟降低17-38%，SLO满意度提升3-6%，准确率仅损失1%。
LASER: Load-Aware Serving with Early-Exit for Reasoning LLMs at the Edge
Large reasoning models (LRMs) such as DeepSeek-R1 have achieved strong performance through extended chain-of-thought (CoT) generation. However, deploying them on edge devices raises a conflict between long CoT sequences and constrained resources. Recent confidence-based early exit methods reduce CoT length for individual requests, yet they apply fixed thresholds from a single-request perspective, ignoring multi-request concurrency and load fluctuation in edge serving. To bridge this gap, we propose \underline{L}oad-\underline{A}ware \underline{S}erving with \underline{E}arly-exit for \underline{R}easoning (LASER). LASER couples two complementary designs: (1) a load-aware adaptive exit threshold that adjusts the confidence bar based on real-time system load within an empirically validated robust range, and (2) a difficulty- and load-aware reasoning budget pre-allocation that assigns compute resources by request difficulty and system capacity. We formulate the problem as a joint optimization of reasoning quality and service latency. Experiments on two reasoning models, four benchmarks, and diverse load conditions show that LASER reduces average latency by 17--38\% and improves service-level objective (SLO) satisfaction by 3--6\% over fixed-threshold baselines, at an average accuracy cost of only 1\%.