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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

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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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