SPAR-K: Scheduled Periodic Alternating Early Exit for Spoken Language Models
Interleaved spoken language models (SLMs) alternately generate text and speech tokens, but decoding at full transformer depth for every step becomes costly, especially due to long speech sequences. We propose SPAR-K, a modality-aware early exit framework designed to accelerate interleaved SLM inference while preserving perceptual quality. SPAR-K introduces a speech alternating-depth schedule: most speech positions exit at a fixed intermediate layer, while periodic full-depth "refresh" steps mitigate distribution shift due to early exit. We evaluate our framework using Step-Audio-2-mini and GLM-4-Voice across four datasets spanning reasoning, factual QA, and dialogue tasks, measuring perfo...
| Authors | Hsiao-Ying Huang, Cheng-Han Chiang, Hung-yi Lee |
|---|---|
| Primary category | cs.CL |
| Published | 10 March 2026 |
| Last updated at source | 27 August 2026 |
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