CLAP Framework Improves Domain Agent Post-training Reliability

Training domain-specific AI agents often encounters issues such as noisy business data, uncertain performance gains after training, discrepancies between offline evaluations and real-world application, and risks associated with releasing new adapters. To address these challenges, researchers introduce CLAP (Closed-Loop Agent Post-training), a comprehensive methodology designed to manage the entire lifecycle of domain agent post-training. CLAP transforms raw business data into structured samples suitable for supervised fine-tuning (SFT), decision-preference learning, holdout sets, and detailed risk diagnostics, including release-gate records. The framework employs a closed-loop approach, combining rigorous data validation, normalization of targets and evidence, reward and KL divergence diagnostics, offline gating mechanisms, and application-chain replay. This integrated system helps determine if a newly trained adapter is truly suitable for its intended application. Experiments on manufacturing scenarios showed that while QLoRA-style LoRA-SFT yielded modest average gains, only a fraction of batches improved, with some even regressing. Crucially, CLAP's application-chain replay highlighted the necessity of Retrieval-Augmented Generation (RAG) for factual extraction and demonstrated how RAG-oriented adapters can improve performance despite increased latency. These findings underscore the importance of a holistic, integrated data-training-evaluation-release loop over relying solely on training completion or single offline metrics.