用强化学习和共享记忆打造更智能的企业业务代理
CRMWeaver: Building Powerful Business Agent via Agentic RL and Shared Memories
- 训练时用合成数据+强化学习，让代理适应复杂业务环境
- 推理时引入共享记忆，提升对新任务的泛化能力
- 轻量模型在真实业务场景中表现媲美大型模型
近年来，基于大语言模型的智能体快速发展，为解决现实世界复杂问题带来希望。尤其在企业业务代理领域，智能体通过工具调用与数据库和内部知识库交互，满足多样用户需求。然而该领域存在数据关系复杂、任务类型多样（如统计查询与知识问答）等挑战。为此，我们提出CRMWeaver，一种增强复杂环境下业务代理的新方法。训练阶段采用合成数据生成与强化学习结合的范式，显著提升模型处理复杂数据和多样化任务的能力；推理阶段引入共享记忆机制，使代理能从类似任务的指导中学习，从而增强有效性与泛化性，尤其在未见场景下表现更优。我们在CRMArena-Pro数据集上验证了该方法的有效性，轻量级模型在B2B与B2C业务场景中均取得具有竞争力的结果，展现出其在实际应用中的价值。
原文摘要 · Abstract (English)
Recent years have witnessed the rapid development of LLM-based agents, which shed light on using language agents to solve complex real-world problems. A prominent application lies in business agents, which interact with databases and internal knowledge bases via tool calls to fulfill diverse user requirements. However, this domain is characterized by intricate data relationships and a wide range of heterogeneous tasks, from statistical data queries to knowledge-based question-answering. To address these challenges, we propose CRMWeaver, a novel approach that enhances business agents in such complex settings. To acclimate the agentic model to intricate business environments, we employ a synthesis data generation and RL-based paradigm during training, which significantly improves the model's ability to handle complex data and varied tasks. During inference, a shared memories mechanism is introduced, prompting the agent to learn from task guidelines in similar problems, thereby further boosting its effectiveness and generalization, especially in unseen scenarios. We validate the efficacy of our approach on the CRMArena-Pro dataset, where our lightweight model achieves competitive results in both B2B and B2C business scenarios, underscoring its practical value for real-world applications.
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