@inproceedings{tsymboi-etal-2026-production,
title = "From Production Traffic to Post-Training: Building a Self-Hosted {LLM} That Covers the Corporate Request Mix",
author = "Tsymboi, Olga and
Stoianov, Dmitrii and
Latypov, Ramil and
Taranets, Danil and
Dryabin, Daniil and
Gashkov, Mikhail and
Zelenkovskiy, Viktor and
Fida, Aleksandr and
Alektorov, Gleb and
Gulyakov, Nikita and
Babkin, Arthur and
Medvedev, Aleksandr and
Gein, Pavel and
Potapov, Anatolii",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.117/",
pages = "1995--2023",
ISBN = "979-8-89176-460-6",
abstract = "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 optimising all objectives jointly, which introduces cross-domain reward interference, we train a separate GRPO expert per axis and merge them via two-stage SLERP. Each expert{'}s reward exposes a distinct failure mode, namely semantic collapse, over-calling, and verbosity hacking, each requiring a domain-specific fix. In non-reasoning mode the recipe surpasses a ${\sim}7\times$ larger by total parameters baseline on the in-house Arena with 69.6 to 65.8, instruction following with 0.85 to 0.83, and function-calling with 0.79 to 0.77, while lifting general dialogue benchmarks. The model absorbs 50{\%} of platform traffic, 116M requests per month, at a fraction of the serving cost."
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<abstract>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 optimising all objectives jointly, which introduces cross-domain reward interference, we train a separate GRPO expert per axis and merge them via two-stage SLERP. Each expert’s reward exposes a distinct failure mode, namely semantic collapse, over-calling, and verbosity hacking, each requiring a domain-specific fix. In non-reasoning mode the recipe surpasses a \sim7\times larger by total parameters baseline on the in-house Arena with 69.6 to 65.8, instruction following with 0.85 to 0.83, and function-calling with 0.79 to 0.77, while lifting general dialogue benchmarks. The model absorbs 50% of platform traffic, 116M requests per month, at a fraction of the serving cost.</abstract>
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%0 Conference Proceedings
%T From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix
%A Tsymboi, Olga
%A Stoianov, Dmitrii
%A Latypov, Ramil
%A Taranets, Danil
%A Dryabin, Daniil
%A Gashkov, Mikhail
%A Zelenkovskiy, Viktor
%A Fida, Aleksandr
%A Alektorov, Gleb
%A Gulyakov, Nikita
%A Babkin, Arthur
%A Medvedev, Aleksandr
%A Gein, Pavel
%A Potapov, Anatolii
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F tsymboi-etal-2026-production
%X 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 optimising all objectives jointly, which introduces cross-domain reward interference, we train a separate GRPO expert per axis and merge them via two-stage SLERP. Each expert’s reward exposes a distinct failure mode, namely semantic collapse, over-calling, and verbosity hacking, each requiring a domain-specific fix. In non-reasoning mode the recipe surpasses a \sim7\times larger by total parameters baseline on the in-house Arena with 69.6 to 65.8, instruction following with 0.85 to 0.83, and function-calling with 0.79 to 0.77, while lifting general dialogue benchmarks. The model absorbs 50% of platform traffic, 116M requests per month, at a fraction of the serving cost.
%U https://aclanthology.org/2026.emnlp-industry.117/
%P 1995-2023
Markdown (Informal)
[From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix](https://aclanthology.org/2026.emnlp-industry.117/) (Tsymboi et al., EMNLP 2026)
ACL
- Olga Tsymboi, Dmitrii Stoianov, Ramil Latypov, Danil Taranets, Daniil Dryabin, Mikhail Gashkov, Viktor Zelenkovskiy, Aleksandr Fida, Gleb Alektorov, Nikita Gulyakov, Arthur Babkin, Aleksandr Medvedev, Pavel Gein, and Anatolii Potapov. 2026. From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1995–2023, Budapest, Hungary. Association for Computational Linguistics.