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New 1.5B router model achieves 93% accuracy without costly retraining
Katanemo Labs' new LLM routing framework aligns with human preferences and adapts to new models without retraining.
As the number of LLMs grows, developers are moving from single-model setups to multi-model systems that use the unique strengths of each model for specific tasks (e.g., code generation, text summarization, or image editing). Performance-based routing, on the other hand, rigidly prioritizes benchmark scores, often neglects real-world user preferences and adapts poorly to new models unless it undergoes costly fine-tuning. “While the length of routing policies can get long, we can easily increase the context window of Arch-Router with minimal impact on latency,” explains Salman Paracha, co-author of the paper and Founder/CEO of Katanemo Labs.
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