Wnuan paper tests staged post-training for enterprise QA
A 32B route lifted acceptable-answer rate on WnuanBench while reducing general-benchmark scores.
Why it matters
The results show staged post-training can substantially improve domain QA performance, but with measurable tradeoffs in general capability. That makes evaluation of both enterprise-task gains and broader regressions central to deployment decisions.
The key points
- 1.Wnuan targets proprietary enterprise question answering with staged post-training.
- 2.A 32B route reached 91.51% acceptable-answer rate after RL.
- 3.General-benchmark average declined 5.17 points across the route.
A new arXiv paper presents Wnuan, a three-stage pipeline for enterprise question answering over proprietary knowledge. The method builds task-oriented supervision from documents, applies supervised fine-tuning with general-data replay, and uses reinforcement learning on residual errors. On the 707-question WnuanBench, the primary 32B route improved acceptable-answer rate from 52.76% before adaptation to 80.06% after SFT and 91.51% after RL, while the general-benchmark average fell 5.17 points.
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