VQV Signal
SpeechGym is a new framework that trains voice agents entirely through speech using reinforcement learning, avoiding reliance on text-based methods or costly proprietary APIs. It allows gradients to flow and supports on-policy learning, improving voice agent performance in multi-turn spoken dialogu...
SpeechGym is a new framework that trains voice agents entirely through speech using reinforcement learning, avoiding reliance on text-based methods or costly proprietary APIs. It allows gradients to flow and supports on-policy learning, improving voice agent performance in multi-turn spoken dialogu...
AI-assisted summary based on the listed source.
Voice agents must call tools and hold multi-turn dialogue entirely through speech, yet the dominant paradigm trains them in text. Existing frameworks either cascade TTS and ASR around a proprietary voice API, where gradients cannot flow and per-call cost makes on-policy reinforcement learning prohibitive, or stay...
Current voice agent training methods either depend on text or expensive, non-differentiable APIs, limiting their ability to improve through reinforcement learning. SpeechGym's audio-native approach offers a more direct and efficient way to train voice agents in realistic spoken interactions.
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VQV surfaced this signal because it is recent, relevant to AI Voice, connected to arXiv.
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