Kyutai releases speech-native math models that skip the text LLM
The released GLM-4-Voice derivatives score up to 77.1% on GSM8K, though larger cascaded systems still lead the benchmark.
By Ryan Merket · Published
Primary source: Kyutai on X
Why it matters
Voice assistants usually trade responsiveness for reasoning quality by sending transcripts through a text model. Kyutai shows that reinforcement learning and overlapping hidden reasoning can narrow that gap inside a speech-native model, while its weaker general-knowledge results show the cost of specializing too aggressively.
Paris-based Kyutai released two speech-native models that take spoken math problems and answer aloud without routing the inference through a transcription system or a separate text language model.
https://x.com/kyutai_labs/status/2102080831242060123
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