AI

Route-and-Generate: KL-Regularized Router Improvement for Reasoning Routing in LLMs

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 23/10/2026, 04:03:24 UTC.

Briefings

Route-and-Generate (Rouge) is a hierarchical training framework for LLMs that decouples reasoning effort allocation from answer generation. It introduces a routing policy allocating a fixed sampling budget across multiple reasoning formats, optimized with GRPO and a rank-based reward favoring shorter correct solutions. This design improves accuracy-efficiency trade-offs by encouraging concise answers on easy problems while reserving longer reasoning for harder ones, as demonstrated across multiple reasoning benchmarks.

Key points

• Rouge uses a hierarchical training framework to decouple reasoning effort allocation from answer generation.
• The routing policy allocates a fixed sampling budget across multiple reasoning formats using a rank-based reward.

Context

Large language models often apply complex reasoning indiscriminately, leading to overthinking on simple tasks and underthinking on harder ones. This framework aims to improve efficiency by adapting reasoning depth based on task difficulty.

Limitations

The evidence is from an arXiv preprint dated 2026-10-23, indicating it is unverified and subject to future revision. Specific benchmark results and performance metrics are not provided in the source.

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