Rubric-Guided Process Reward for Stepwise Model Routing
Quick summary
arXiv:2605.29310v2 Announce Type: replace Abstract: Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model. Recent methods formulate routing as a sequential decision process and train the router with reinforcement learning. However, although they model routing as a process, they still supervise the router with outcome rewards. Such rewards only reflect final answer correctness and fail to evaluate intermediate routing decisions, which can weaken performance and generalization. To address this gap, we propose RoRo, a
Key takeaways
- arXiv:2605.29310v2 Announce Type: replace Abstract: Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model.
- Recent methods formulate routing as a sequential decision process and train the router with reinforcement learning.
- However, although they model routing as a process, they still supervise the router with outcome rewards.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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