Retrieval-augmented generation (RAG) connects large language models (LLMs) to external knowledge, but single-round retrieval is often insufficient for complex multi-hop questions. To improve search on complex tasks, most existing works combine multi-round retrieval with reasoning through end-to-end training. While these approaches improve problem-solving performance, they still face challenges in task reasoning and model training. First, due to the lack of an explicit subgoaling mechanism, most existing RAG methods may produce ambiguous retrieval execution paths, leading to the degradation of model performance. Second, a few existing RAG methods have started to incorporate planning and execution into the model training process. However, they typically rely on a single final reward, causing hierarchical credit entanglement between planning and execution and preventing the model from clearly separating plan errors from retrieval errors. To address these limitations, we propose APEX-Searcher, a coordinated planner–executor framework that optimizes a recursive planner with RL to accommodate multiple valid, dependency-aware decompositions while training the executor with SFT under clearer subtask-level supervision. Extensive experiments show consistent gains in both multi-hop RAG and task planning across benchmarks.

The end-to-end task-oriented dialogue system has achieved great success in recent years. Most of these dialogue systems need to accommodate multi-domain dialogue in real-world scenarios. However, due to the high cost of dialogue data annotation and the scarcity of labeled dialogue data, existing methods are difficult to extend to new domains. Therefore, it is important to use limited data to construct multi-domain dialogue systems. To solve this problem, we propose a novel domain attention module. It use the distributional signatures to construct a multi-domain dialogue system effectively with limited data, which has strong extensibility. We also define a adjacent n-gram pattern to explore potential patterns for dialogue entities. Experimental results show that our approach outperforms the baseline models on most metrics. In the few-shot scenario, we show our method get a great improvement compared with previous methods while keeping smaller model scale.