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    <abstract>Mixture-of-Experts (MoE) models reduce computation by routing each token to a small set of experts, but each selected expert is still used at a fixed precision. We present DPA-MoE, a runtime precision control framework that makes routed experts any-precision. DPA-MoE encodes expert feed-forward weights as prefix-decodable chunks using SharedScale AnyBCQ, profiles per-expert reconstruction residuals offline, and allocates chunks at inference time using live router gate scores. This post-routing allocation lets a single encoded MoE checkpoint operate at multiple effective weight budgets without retraining or re-encoding. Experiments on Qwen3-30B-A3B and DeepSeekMoE-16B show that DPA-MoE improves KL divergence and downstream accuracy over uniform chunk allocation at matched budgets, with modest steady-state throughput overhead in our prototype.</abstract>
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%0 Conference Proceedings
%T DPA-MoE: A Runtime Precision Control Framework for MoE Inference
%A Kim, Jaeyun
%A Lee, Hoonki
%A Shin, Susie
%A Kim, Hyunmin
%A Lee, Dahyun
%A Kim, Sangyeob
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F kim-etal-2026-dpa
%X Mixture-of-Experts (MoE) models reduce computation by routing each token to a small set of experts, but each selected expert is still used at a fixed precision. We present DPA-MoE, a runtime precision control framework that makes routed experts any-precision. DPA-MoE encodes expert feed-forward weights as prefix-decodable chunks using SharedScale AnyBCQ, profiles per-expert reconstruction residuals offline, and allocates chunks at inference time using live router gate scores. This post-routing allocation lets a single encoded MoE checkpoint operate at multiple effective weight budgets without retraining or re-encoding. Experiments on Qwen3-30B-A3B and DeepSeekMoE-16B show that DPA-MoE improves KL divergence and downstream accuracy over uniform chunk allocation at matched budgets, with modest steady-state throughput overhead in our prototype.
%R 10.18653/2026.findings-emnlp.1076
%U https://aclanthology.org/2026.findings-emnlp.1076/
%U https://doi.org/10.18653/2026.findings-emnlp.1076
%P 22761-22778

Markdown (Informal)

[DPA-MoE: A Runtime Precision Control Framework for MoE Inference](https://aclanthology.org/2026.findings-emnlp.1076/) (Kim et al., Findings 2026)

• DPA-MoE: A Runtime Precision Control Framework for MoE Inference (Kim et al., Findings 2026)

ACL

• Jaeyun Kim, Hoonki Lee, Susie Shin, Hyunmin Kim, Dahyun Lee, and Sangyeob Kim. 2026. DPA-MoE: A Runtime Precision Control Framework for MoE Inference. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 22761–22778, Budapest, Hungary. Association for Computational Linguistics.