Wnuan: Staged Post-Training for Question Answering over Proprietary Enterprise Knowledge

Abstract:Enterprise question answering requires models to acquire proprietary knowledge without discarding general capabilities. We present Wnuan, a three-stage pipeline that constructs task-oriented supervision from documents, performs supervised fine-tuning with general-data replay, and applies reinforcement learning to residual errors. On the 707-question WnuanBench, the primary 32B route raises acceptable-answer rate (AAR) from 52.76% before adaptation to 80.06% after SFT and 91.51% after RL. Under a matched 100-update protocol, residual-error sampling outperforms full-pool and size-matched random sampling by 3.11 and 2.97 points, respectively. Source-cluster bootstrap intervals remain above zero for both contrasts, and a same-domain validation set preserves the ordering. The general-benchmark average decreases by 5.17 points across the route, concentrated in instruction following. The automatic evaluation ensemble agrees with an authoritative domain expert on 90.5% of a stratified Wnuan-Inst response sample. These results characterize both the gains and the general-capability cost of staged enterprise adaptation.

22 pages, 10 figures. Includes Supplementary Appendices A--L. Xiaofeng Shi and Xiaosong Qiu contributed equally

Artificial Intelligence (cs.AI)

arXiv:2608.01862 [cs.AI]

(or arXiv:2608.01862v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.01862 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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