Adapting Technical-Service LLM Agents with Latent Logic Augmentation, Robust Noise Reduction, and Hybrid Reward Modeling

The paper proposes a framework for improving technical‑service large language model agents by integrating latent logic augmentation, robust noise reduction, and hybrid reward modeling. It enhances supervised fine‑tuning data with Planning‑Aware Trajectory Modeling and Reasoning Augmentation, creates dual‑filtered multiple ground truths, and trains the policy using a hybrid reward that combines a reranker with an LLM‑as‑a‑Judge. Applied to real Cloud technical‑service tasks, the adapted Qwen3‑4B model achieves the highest Multi‑ECS score, reduces reward cost, and attains the highest production adoption rate.

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• llms
• agents
• reinforcement-learning
• fine-tuning