Rubric-Guided Process Reward for Stepwise Model Routing
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Route-and-Generate (Rouge) is a hierarchical training framework that lets large language models decide how much reasoning to apply to each input. It separates reasoning effort from answer generation by using a routing policy that distributes a fixed sampling budget across different reasoning formats, while the base model is trained with GRPO and per-sample rewards. The method employs a rank‑based reward favoring shorter correct solutions and updates the router through a closed‑form KL‑regularized policy improvement, leading to more concise answers on easy tasks and longer reasoning on harder ones, thereby improving accuracy‑efficiency trade‑offs on multiple reasoning benchmarks.
The paper demonstrates that expert routing statistics in Mixture-of-Experts (MoE) models can serve as an intrinsic process-confidence signal, rivaling conventional output-logit confidence. Leveraging this insight, the authors develop RoCo-Rethink, which selectively regenerates uncertain reasoning steps, and RoCo-PRM, which automatically annotates reasoning trajectories using routing-derived step scores and distills them into a standalone process reward model. Across various MoE architectures and reasoning tasks, both methods outperform strong baselines and transfer effectively between models and tasks.
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