In dialogue systems, role-playing agents (RPAs) aim to embody specific characters, but they often struggle to exhibit emotional expressions that are consistent with their assigned roles, resulting in insufficient emotional fidelity. This paper proposes EmoRL, a novel curriculum reinforcement learning (RL) framework, to enhance the emotional fidelity of RPAs. EmoRL features a multi-stage training paradigm guided by a carefully designed suite of reward functions. These rewards, including Format, Fine-grained Exact (FE), Emotional Similarity (ES), and Coarse-grained Categorical (CC) rewards, assess emotional consistency from strict to lenient perspectives. The curriculum RL process applies these rewards sequentially from easy (CC) to difficult (FE), enabling the RPAs to progressively learn coarse-grained emotional tendencies before mastering fine-grained distinctions. Furthermore, we introduce an emotional dialogue data synthesis pipeline to augment training data diversity and balance. Experiments on the Personality EmotionLines Dataset (PELD) demonstrate that EmoRL significantly outperforms baselines like RoleLLM, SFT, and DPO across both single-turn and multi-turn dialogues, with particularly notable gains in longer conversations. Our findings confirm the effectiveness of the curriculum learning strategy, the importance of balanced data, and the value of synthesized data for improving emotional fidelity.

Retrieval-Augmented Generation (RAG) has emerged as an important means of enhancing the performance of large language models (LLMs) in knowledge-intensive tasks. However, most existing RAG strategies treat retrieved passages in a flat and unstructured way, which prevents the model from capturing structural cues and constrains its ability to synthesize knowledge from dispersed evidence across documents. To overcome these limitations, we propose Disco-RAG, a discourse-aware framework that explicitly injects discourse signals into the generation process. Our method constructs intra-chunk discourse trees to capture local hierarchies and builds inter-chunk rhetorical graphs to model cross-passage coherence. These structures are jointly integrated into a planning blueprint that conditions the generation. Experiments on question answering and long-document summarization benchmarks show the efficacy of our approach. Disco-RAG achieves state-of-the-art results on the benchmarks without fine-tuning. These findings underscore the important role of discourse structure in advancing RAG systems.

Role-playing agents (RPAs) powered by large language models (LLMs) have been widely utilized in dialogue systems for their capability to deliver personalized interactions. Current evaluations of RPAs mainly focus on personality fidelity, tone imitation, and knowledge consistency, while overlooking emotional fidelity, a key factor that affects user experience. To this end, we propose a benchmark called EmoCharacter to assess emotional fidelity of RPAs in dialogues. EmoCharacter includes two benchmark datasets (single-turn and multi-turn dialogues), three evaluation settings, and six metrics to measure the emotional fidelity between RPAs and the characters they portray. Based on EmoCharacter, we conduct extensive evaluations on RPAs powered by seven widely used LLMs with representative role-playing methods. Our empirical findings reveal that: (1) Contrary to intuition, current role-playing methods often reduce the emotional fidelity of LLMs in dialogues; (2) Enhancing the general capabilities of LLMs does not necessarily improve the emotional fidelity of RPAs; (3) Fine-tuning or In-Context Learning based on real dialogue data can enhance emotional fidelity.