Recently, the generative retrieval paradigm has emerged as a transformative framework that significantly enhances the efficiency of large-scale industrial recommendation systems. This innovative approach systematically maps items to meaningful semantic identifiers (SIDs) and employs advanced sequence generation techniques to construct high-quality candidate sets, thereby enabling more accurate modeling of users' evolving interests and behavioral patterns. Nevertheless, two critical challenges remain inadequately addressed in current research: (1) Existing methodologies predominantly focus on modeling a single task such as predicting users' click behavior, while overlooking other tasks including predicting users' dwell-time and engagement behaviors, which are very important for video/content recommendation at the same time. The independent modeling of each task inevitably results in substantial computational overhead, thereby raising the pivotal question of whether the sophisticated multi-task learning capabilities inherent in LLMs can be effectively leveraged to achieve unified and efficient multi-task learning for generative retrieval. (2) The mapping mechanism from SIDs to concrete items requires substantial refinement to ensure precise and reliable retrieval performance. To tackle these issues, we propose RedGR, a generative retrieval model that unifies the modeling of multiple complex retrieval tasks. RedGR first applies the RQ-Kmeans algorithm to map items into SIDs, and then conducts pre-training on large-scale user behavior datasets to learn general knowledge. Then the RedGR model is finetuned on multi-task retrieval data with a unique instruction prompt for each task. This enables RedGR to generate the corresponding set of SIDs for each task. And the union of all sets of SIDs is the multi-task retrieval result. Finally, the Swing algorithm incorporates explicit, high-quality collaborative signals to strengthen the mapping from SIDs to specific items, thereby facilitating efficient retrieval of high-quality items. RedGR has been fully depolyed in the homefeed recommendation scenario of RedNote,serving hundreds of millions of users every day. Online A/B test results show a 0.178% increase in pagetime, a 0.734% increase in average user engagement, and a 0.076% growth in homefeed active users (FAU). These metrics collectively validate the superior performance of RedGR's unified retrieval modeling approach in complex multi-task scenarios.