@inproceedings{wen-etal-2026-outlets,
title = "{OUTLETS}: Output-Length Prediction from Speculative Decoding Backbones",
author = "Wen, Weihuang and
Liu, Yingying and
Liu, Yichuan and
Zeng, Wenqi and
Zhou, Li and
Sun, Chumin and
Sun, Jie and
Yu, Tianshu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.366/",
pages = "8385--8401",
ISBN = "979-8-89176-459-0",
abstract = "The heavy-tailed distribution of output lengths in Large Language Model (LLM) serving poses major challenges for resource provisioning and cluster scheduling. Although output-length prediction can mitigate these issues, existing approaches have key drawbacks: external proxy models add substantial latency and often have limited fidelity, whereas internal state-based methods are efficient but rely on shallow probes of current model states. We identify a structural connection between speculative decoding (SD) and length prediction: latent representations produced by the draft decoder in advanced frameworks (e.g., EAGLE-3) encode signals that are predictive of generation length. Building on this insight, we introduce **OUTLETS** (**Out**put-**Le**ng**t**h Prediction from **S**peculative Decoding Backbones), which repurposes the speculative backbone as a trajectory-aware length predictor. When its draft representations are already computed for speculative decoding, OUTLETS adds only a lightweight regression head and achieves lower MAE than the evaluated methods. Under saturated disaggregated serving, OUTLETS predictions enable standard scheduling policies to prioritize shorter requests and distribute requests more evenly across decoding instances, reducing short-request P99 latency by **34.8{\%}**."
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<abstract>The heavy-tailed distribution of output lengths in Large Language Model (LLM) serving poses major challenges for resource provisioning and cluster scheduling. Although output-length prediction can mitigate these issues, existing approaches have key drawbacks: external proxy models add substantial latency and often have limited fidelity, whereas internal state-based methods are efficient but rely on shallow probes of current model states. We identify a structural connection between speculative decoding (SD) and length prediction: latent representations produced by the draft decoder in advanced frameworks (e.g., EAGLE-3) encode signals that are predictive of generation length. Building on this insight, we introduce **OUTLETS** (**Out**put-**Le**ng**t**h Prediction from **S**peculative Decoding Backbones), which repurposes the speculative backbone as a trajectory-aware length predictor. When its draft representations are already computed for speculative decoding, OUTLETS adds only a lightweight regression head and achieves lower MAE than the evaluated methods. Under saturated disaggregated serving, OUTLETS predictions enable standard scheduling policies to prioritize shorter requests and distribute requests more evenly across decoding instances, reducing short-request P99 latency by **34.8%**.</abstract>
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%0 Conference Proceedings
%T OUTLETS: Output-Length Prediction from Speculative Decoding Backbones
%A Wen, Weihuang
%A Liu, Yingying
%A Liu, Yichuan
%A Zeng, Wenqi
%A Zhou, Li
%A Sun, Chumin
%A Sun, Jie
%A Yu, Tianshu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wen-etal-2026-outlets
%X The heavy-tailed distribution of output lengths in Large Language Model (LLM) serving poses major challenges for resource provisioning and cluster scheduling. Although output-length prediction can mitigate these issues, existing approaches have key drawbacks: external proxy models add substantial latency and often have limited fidelity, whereas internal state-based methods are efficient but rely on shallow probes of current model states. We identify a structural connection between speculative decoding (SD) and length prediction: latent representations produced by the draft decoder in advanced frameworks (e.g., EAGLE-3) encode signals that are predictive of generation length. Building on this insight, we introduce **OUTLETS** (**Out**put-**Le**ng**t**h Prediction from **S**peculative Decoding Backbones), which repurposes the speculative backbone as a trajectory-aware length predictor. When its draft representations are already computed for speculative decoding, OUTLETS adds only a lightweight regression head and achieves lower MAE than the evaluated methods. Under saturated disaggregated serving, OUTLETS predictions enable standard scheduling policies to prioritize shorter requests and distribute requests more evenly across decoding instances, reducing short-request P99 latency by **34.8%**.
%U https://aclanthology.org/2026.emnlp-main.366/
%P 8385-8401
Markdown (Informal)
[OUTLETS: Output-Length Prediction from Speculative Decoding Backbones](https://aclanthology.org/2026.emnlp-main.366/) (Wen et al., EMNLP 2026)
ACL
- Weihuang Wen, Yingying Liu, Yichuan Liu, Wenqi Zeng, Li Zhou, Chumin Sun, Jie Sun, and Tianshu Yu. 2026. OUTLETS: Output-Length Prediction from Speculative Decoding Backbones. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 8385–8401, Budapest, Hungary. Association for Computational Linguistics.