Real-world inference tasks for large language models exhibit diverse difficulty levels. Existing LLM serving systems integrate models of different sizes and attempt to route tasks of appropriate difficulty to the most suitable model, aiming to reduce resource waste while guaranteeing service quality. Such systems usually adopt a cascading architecture, which performs inference sequentially from lightweight models to heavyweight models and validates outputs until a model that meets the task requirements is identified. However, when handling complex tasks, the cascading architecture inevitably processes unnecessary small models first, leading to cumulative latency and redundant resource consumption. This paper proposes ParaCascade, a parallel cascading framework that supports early routing. The core idea of ParaCascade is to bypass lightweight models and directly route difficult instances to heavyweight model tiers by pre-estimating task complexity, thus avoiding ineffective computation on lightweight models. In addition, ParaCascade adopts parallel prediction and model parallel inference strategies. At the cost of a slight increase in energy consumption, it significantly reduces the systemic latency caused by sequential processing, thereby improving the overall QoS. Extensive evaluations across diverse workloads on the MMLU-pro and MATH benchmarks show that ParaCascade significantly outperforms both single-model deployments and serial inference serving baselines. While maintaining answer quality, it achieves an inference speedup of 1.16× to 1.51×, demonstrating its superiority in efficient LLM serving systems.
Hao Wei, Lujia Yin, Chen Chen et al.· Fall Joint Computer Conferen...· 0 citations
Multi-turn retrieval-augmented generation (RAG) improves question answering by decomposing evidence seeking into iterative retrieval and reasoning steps. Existing multi-turn RAG methods usually optimize when and how to retrieve while fixing the number of retrieved documents per step. However, we discovered that this fixed-TopK design is suboptimal: single-hop questions tend to benefit from fewer retrieval rounds with larger per-round evidence sets, whereas multi-hop questions require more retrieval rounds with smaller evidence sets to support stepwise reasoning. To bridge this gap, we introduce AdaRAG, a budget-aware adaptive RAG framework that learns how to retrieve under a hard document budget, including how many retrieval rounds to perform, how many documents to retrieve in each round, and which retrieval source to use. AdaRAG implements this idea with a two-level policy architecture. ModeHead, a lightweight retrieval-mode classifier, selects passage retrieval, graph retrieval, or answer generation; TopkHead, a budget-aware document-allocation classifier, selects a legal TopK after query generation according to the remaining budget. These discrete policy heads are decoupled from language-model token generation, enabling direct reinforcement-learning optimization through hierarchical GRPO after supervised action-format learning. Our experiments across five QA benchmarks demonstrate AdaRAG's good generalization performance under constrained document budgets. In detailed comparisons on HotpotQA, it surpasses the strongest baselines by an average of 10.8 percentage points in Exact Match (EM) and F1 score.
Jia-Nan Sun, Miao Zhang, Chen Chen et al.· Fall Joint Computer Conferen...· 0 citations