PersonaPlex

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1. 3d
   HiPLEX: Hierarchical Policy Factorization for Full-Duplex Speech Language Models

   The paper introduces HiPLEX, a reinforcement learning framework that factorizes a pretrained full-duplex text policy into a control policy deciding when to emit content (choosing among pad, epad and con) and a conditional content policy that picks a token only when con is selected. The authors report that across three Moshi seeds on Full-Duplex-Bench v1, HiPLEX reduces takeover rates during natural user pauses and backchannel opportunities and shortens post-interruption response latency versus GRPO, while keeping comparable judged interruption-response quality, and better matches pooled human turn-timing and backchannel-rate marginals on Moshi and PersonaPlex.

   Hugging Face · Papers · 🔥 0

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