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arXiv · 2609.12310

ESTS at WMT26: Routing-Informed Expert Pruning for Model Compression

Liu O. Martin·Lucas Bandarkar·Nanyun Peng
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Abstract

We describe six submissions under the team name ESTS to the unconstrained WMT26 Model Compression Shared Task for English--Simplified Chinese and English--Egyptian Arabic. We submit three compression operating points per translation direction, all derived from GPT-OSS-20B. We use task-specific routing mass to rank experts and cross-lingual routing divergence to allocate retained capacity across layers, then physically remove low-importance experts. The resulting specialists are recovery-tuned on GPT-5.1-generated synthetic translation data and further compressed by applying MXFP4 quantization to the retained expert projection weights. We additionally implement a robust inference system for the instruction-conditioned WMT26 setting, including category inference, output validation, retries, segmented fallback, and source-owned JSON reconstruction. Across our six submissions, parameter counts range from 4.186B to 7.770B and packed artifact sizes from 4.55 to 6.33~GiB. Internal xCOMET-XL evaluation using GPT-5.1 pseudo-references provides an internal comparison across the submitted compression operating points.

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BibTeXRIS

Liu O. Martin, Lucas Bandarkar, Nanyun Peng. 2026-09-11. ESTS at WMT26: Routing-Informed Expert Pruning for Model Compression. https://arxiv.org/abs/2609.12310

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