Role-playing agents (RPAs) must sustain persona fidelity, natural interaction, and scene progression across multi-turn trajectories. However, supervised fine-tuning, preference optimization, and coarse episode-level RL can over-reinforce safe stylized responses that appear in-character but are repetitive, low-information, or weakly grounded in the evolving scene. We propose LSH-RL, a Long-Short Horizon Reinforcement Learning framework for persona-level RPAs. LSH-RL trains a shared policy through dynamic multi-role self-play, combining short-horizon utterance/action rewards with long-horizon character-trajectory rewards. A gated trajectory-to-turn reward fusion mechanism prevents locally degenerate turns from receiving undeserved long-horizon credit, while a dialogue-action curriculum supports staged optimization. Experiments on PersonaArena and CharacterBox show that LSH-RL improves over SFT and DPO in both dialogue-only and full interaction settings. Further analyses show reduced repetition and stronger human preferences, suggesting a path toward more coherent and interaction-rich persona-level RPAs. The source code is available at https://github.com/Shiwenlong-source/LSH-RL.

Role-playing capabilities in large language models (LLMs) often lack cognitive consistency in complex scenarios that require deep understanding and coherent reasoning. While recent reasoning models excel in math and coding tasks, they show limited effectiveness in open-ended role-playing scenarios. We introduce R-CHAR (Role-Consistent Hierarchical Adaptive Reasoning), a metacognition-driven framework that enhances role-playing performance through guided thinking trajectories synthesis and adaptive evaluation. Our approach demonstrates that concise thinking processes can achieve superior performance efficiently compared to elaborate reasoning chains in role-playing social intelligence tasks, outperforming existing specialized models. Experimental results on the SocialBench benchmark show significant and stable performance improvements across varying scenario complexities, showing particular strength in long-context comprehension (from 34.64% to 68.59%) and group-level social interactions. Our work advances the development of cognitively consistent role-playing systems, bridging the gap between surface-level mimicry and authentic character simulation.

Efficient processing of long contexts in large language models (LLMs) is essential for real-world applications like retrieval-augmented generation and in-context learning, especially in resource-constrained environments such as edge computing. This paper explores the embedding-based context compression to reduce inference costs while preserving the downstream LLM configurations. We propose a decoupled compressor-LLM framework, pretrained on text reconstruction and completion tasks, designed to effectively preserve essential contextual information within condensed embedding representations. Our extensive experiments investigate pretraining, model configurations, compression rates, efficiency across tasks, and adaptability to various LLMs. Results demonstrate that our approach outperforms competitive baselines in three domains and across eight datasets while being adaptable to different downstream LLMs. We find that thorough pretraining and carefully selected compression rates, such as 4x and 16x, enable a lightweight compressor to achieve a good balance between accuracy and speed. These findings underscore the potential of embedding-based compression to enhance LLM efficiency and motivate further research in this area.