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.
Why it matters: Turn-taking and interruption handling are central pain points for real-time voice agents, and separating timing from content optimization offers a reusable training idea.
Computer Science > Sound
arXiv:2610.07727 (cs)
[Submitted on 6 Oct 2026]
Title:HiPLEX: Hierarchical Policy Factorization for Full Duplex Speech Language Models
Authors:Kyudan Jung, Hyunsin Park, Yoonhyung Lee, Jinhwan Park, Jinhyeok Yang, KiHyun Nam, Jaegul Choo, Jinkyu Lee
View a PDF of the paper titled HiPLEX: Hierarchical Policy Factorization for Full Duplex Speech Language Models, by Kyudan Jung and 7 other authors
View PDF HTML (experimental)Abstract:As human--AI interactions become more conversational, full-duplex speech language models capable of natural real-time dialogue are growing in importance. Beyond generating appropriate responses, these models must coordinate turn-taking, backchanneling, and floor management in real time. Reinforcement learning (RL) provides a way to refine these behaviors through direct feedback on interaction outcomes. However, existing RL methods either apply timing feedback to a token policy or optimize semantic content, leaving the joint improvement of timing and content unresolved. We introduce HiPLEX, an RL framework that factorizes a pretrained full-duplex text policy into a control policy that decides when to emit content and a conditional content policy that decides what to emit. The first factor selects among 'pad', 'epad', and 'con'. The second selects a token only when 'con' is chosen. This hierarchy describes conditional actions within each frame and uses the model's existing text head. We route timing advantages to the token-group factor through event-causal masks derived from generated speech episodes, and route an LLM-judge semantic advantage to the conditional content factor. 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 relative to GRPO, while maintaining comparable judged interruption-response quality. On Moshi and PersonaPlex, HiPLEX better matches pooled human turn-timing and backchannel-rate marginals than GRPO.
| Comments: | 34 pages, 9 figures, 17 tables, |
| Subjects: | Sound (cs.SD) |
| Cite as: | arXiv:2610.07727 [cs.SD] |
| (or arXiv:2610.07727v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07727 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kyudan Jung [view email][v1] Tue, 6 Oct 2026 04:23:23 UTC (340 KB)
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How we got here
- KLPO: a critic-free, KL-regularized policy optimization method for LLM agentsHugging Face · Papers · GRPO