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From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Data-residency constraints force enterprises to self-host LLMs, but continuous adoption of newer models without decommissioning their predecessors expands the serving fleet, fragmenting a finite GPU pool. We consolidate traffic from over 200 internal applications onto a single model by closing quality gaps identified through production error analysis along three axes: instruction following, function-calling, and internal task distribution. Quality is tracked by offline benchmarks stratified to production traffic and scored by deterministic verifiers or calibrated LLM judges. Rather than optimi

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