Train What You Deploy:Token-Faithful Post-Training of a Production Coding

To address the token and control fidelity errors present in existing code generation models during deployment, researchers proposed a fidelity-aware training coupling framework and the Certified Divergent Proximal Policy Optimization (C-DPPO) algorithm. This framework retains original prompt sampling, uses a negotiation training protocol to eliminate false model calls, and limits loss calculation to verifiable token spans; it also establishes tight bilateral total variation certification bounds, adaptive K-rules, budget-aware sequence guarantees, and fault-tolerant policy masks. Matching training and testing on the TMax-100 dataset for the Baize5B and Baize10B models showed that C-DPPO achieved consistent performance improvements of 3.0 points across model sizes compared to standard DPPO. Certificate audits verified the reliability and full-featured coverage of this certification training pipeline.