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

The ESTS team submitted six entries to the unconstrained WMT26 Model Compression Shared Task for English–Simplified Chinese and English–Egyptian Arabic, all derived from GPT-OSS-20B, with parameter counts ranging from 4.186B to 7.770B and packed artifact sizes from 4.55 to 6.33 GiB. The team ranked experts using task-specific routing mass and allocated retained capacity across layers via cross-lingual routing divergence before physically removing low-importance experts, then recovery-tuned the specialists on GPT-5.1-generated synthetic translation data and applied MXFP4 quantization to the retained expert projection weights. Internal xCOMET-XL evaluation using GPT-5.1 pseudo-references provided a comparison across the submitted compression operating points.

arXiv:2609.12310v1 Announce Type: new 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.