SOURCE-LINKED INTELLIGENCE

Voice of Reason: Reinforcement Learning for Spoken Math

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

Speech language models enable richer spoken interactions between humans and machines than cascaded systems, allowing access to paralinguistic information and lower latency. However, their accuracy on mathematical reasoning benchmarks has lagged behind those of text models. Reinforcement learning (RL) with verifiable rewards has been instrumental in extending text models' capabilities for solving complex problems and limiting hallucinations. In this work, we explore applying RL to the GLM-4-Voice speech model (Zeng et al., 2024) to bridge the gap between textual and spoken mathematical problem

Read original source ↗ Open in workspace

recordType

paper

region

Global