Large language model (LLM) routing assigns each query to the most suitable model from an ensemble. We introduce LLMRouterBench, a large-scale benchmark and unified framework for LLM routing. It comprises over 400K instances from 21 datasets and 33 models. Moreover, it provides comprehensive metrics for both performance-oriented and performance-cost trade-off routing, and integrates 10 representative routing baselines. Using LLMRouterBench, we systematically re-evaluate the field. While confirming strong model complementarity—the central premise of LLM routing—we find that many routing methods exhibit similar performance under unified evaluation, and several recent approaches, including commercial routers, fail to reliably outperform a simple baseline. Meanwhile, a substantial gap remains to the Oracle, driven primarily by persistent model-recall failures. We further show that backbone embedding models have limited impact, that larger ensembles exhibit diminishing returns compared to careful model curation, and that the benchmark also enables latency-aware analysis. All code and data are available at https://github.com/ynulihao/LLMRouterBench.

We propose Process-Aware Policy Optimization (PAPO), a method that integrates process-level evaluation into Group Relative Policy Optimization (GRPO) through **decoupled advantage normalization**, to address two limitations of existing reward designs. Outcome reward models (ORM) evaluate only final-answer correctness, treating all correct responses identically, and gradually lose the advantage signal as groups become uniformly correct. Rubric-based process reward models (PRM) offer richer supervision, differentiating quality among correct responses and restoring advantage diversity, but using their scores as rewards triggers verbosity-driven reward hacking and accuracy collapse. PAPO resolves both by composing the advantage from an outcome advantage A_out, derived from outcome reward r^out over all responses, and a process advantage A_proc, derived from process reward r^proc exclusively over the correct subset. A_out anchors correctness, and correct subset normalization on A_proc rules out verbosity-driven hacking while sustaining the gradient on all-correct groups where ORM exhausts. PAPO consistently outperforms ORM across five model configurations and six benchmarks (reaching 51.3% vs. 46.3% on OlympiadBench for Qwen2.5-7B while continuing to improve as ORM plateaus and declines), with analogous gains when composed with mainstream GRPO variants.