Towards Unified Affective Reasoning via In-Context Reinforcement Learning

Recent advances in Large Language Models (LLMs) have shifted affective computing from direct label prediction to deliberative reasoning, with Reinforcement Learning (RL) emerging as a promising means of cultivating such capabilities. However, existing RL-based methods are largely confined to single-task settings, requiring separate training pipelines for different affective domains and limiting transfer across related problems. To address this limitation, we propose Unified Affective Reasoning (UAR), an in-context reinforcement learning framework for learning generalizable affective reasoning under a unified multi-task paradigm. UAR jointly optimizes multiple affective tasks and leverages in-context demonstrations to reconcile heterogeneous annotation schemas across datasets, thereby aligning disparate label spaces while preserving task-specific supervision. Experiments on 16 standard benchmarks across emotion recognition, aspect-based sentiment analysis, stance detection, and sarcasm detection show that UAR consistently improves the underlying base models and generalizes effectively to held-out out-of-domain tasks. These results demonstrate that in-context reinforcement learning provides an effective path toward unified and generalizable affective reasoning. Code is available at https://github.com/farisxiong/UAR.