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arxiv:2608.01862

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

Published on Aug 3
· Submitted by
XiaofengShi
on Aug 4
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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.

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Paper submitter

The value of this work lies not in proposing another isolated algorithm, but in turning enterprise knowledge injection into a systematic, verifiable, and reusable post-training pipeline: task-oriented Document-to-QA construction, SFT with general-data replay, and residual-error-focused GRPO. Wnuan improves the AAR of a 32B model from 52.76% to 91.51%, while residual-error sampling significantly outperforms full-pool and random sampling under the same update budget. Importantly, the paper also examines the associated loss in instruction-following ability and shows that RAG and model specialization are not necessarily additive. It contributes more than a high-performing model: it offers an evidence-driven methodology for deploying proprietary enterprise knowledge while balancing accuracy, training efficiency, and capability retention.

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