Kyutai Releases Voice of Reason: A Speech-Native Model that Solves Spoken Math with Reinforcement Learning
Kyutai has released Voice of Reason, 2 open-weight speech-to-speech models that solve math problems out loud. Both start from GLM-4-Voice-9B and add supervised fine-tuning (SFT) and reinforcement learning (RL). There is no transcription step and no separate text LLM in the loop. On spoken GSM8K, accuracy climbs from 27.3% for the base model to 77.1%. Is it deployable? Yes, for self-hosting. Kyutai ran both BF16 checkpoints on a single H100. You also need the GLM-4-Voice repo for its speech t
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Kyutai released open-weight speech models that solve math problems spoken aloud without converting speech to text first. The models combine supervised fine-tuning and reinforcement learning to improve math reasoning accuracy from 27.3% to 77.1% on a spoken math benchmark. Speech-native models face challenges because they must emit audio at regular intervals to stay interactive, which limits how much hidden reasoning they can do, whereas traditional cascaded pipelines that convert speech to text, process it, and convert back to speech can reason longer but add latency and lose tone cues. The two released checkpoints run on a single graphics processor and are available as open weights.
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