Routing Should Pay for Itself: Sparse Supervision for Economical LLM Routing
SaveRouter trains LLM routers on 33–41% of feedback and shortens cost payback by up to 9.5 times.
SaveRouter learns an LLM router with sparse supervision, collecting only informative model feedback and sharing capability signals across related queries. On four routing benchmarks it uses about 33–41% of available training feedback while matching or beating conventional routers. The authors report the break-even deployment volume falls by about 1.9–9.5 times versus the fastest conventional router, and note that the cheapest supervision level can differ from the earliest payback point.
- Uses about 33–41% of available training feedback
- Cuts break-even deployment volume by roughly 1.9–9.5 times
- Extra supervision is not always the cheapest choice
- Code is public at LAMDA-Model-Reuse/SaveRouter
- Products
- SaveRouter
- Organizations
- LAMDA-Model-Reuse
From the original article
1 min read at huggingface.co
Large language model (LLM) routing reduces serving cost by assigning each query to an appropriate model while preserving response quality. Learning such a router, however, often requires executing multiple candidate models on historical queries to collect query--model quality feedback, creating a nontrivial supervision cost before deployment.
By Guannan Lai, Gelin Bian, Hao-Xuan Ma · huggingface.co
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.