# RideSkill: A Hierarchical Algorithm for Generalized Ride Sharing with LLM-Driven Automatic Evolution

arXiv: https://arxiv.org/abs/2609.02250 · HTML: https://mlsanity.com/paper/2609.02250/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 2 Sep 2026.

## Builds on (most cited first, 21 of 21)

- [GLM-5: from Vibe Coding to Agentic Engineering](https://mlsanity.com/paper/2602.15763/) (2026)
- [GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization](https://mlsanity.com/paper/2601.05242/) (2026)
- [Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement](https://mlsanity.com/paper/1901.10964/) (2019)
- [Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1901.11454/) (2019)
- [DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03882/) (2019)
- [A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives](https://mlsanity.com/paper/2503.13415/) (2025)
- [Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems](https://mlsanity.com/paper/2212.07313/) (2022)
- [A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications](https://mlsanity.com/paper/2605.07358/) (2026)
- [Real-time City-scale Ridesharing via Linear Assignment Problems](https://mlsanity.com/paper/1902.10676/) (2019)
- [Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems](https://mlsanity.com/paper/2312.08884/) (2023)
- [An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing](https://mlsanity.com/paper/2408.10479/) (2024)
- [BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch](https://mlsanity.com/paper/2501.13448/) (2025)
- [One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms](https://mlsanity.com/paper/2507.15351/) (2025)
- [Real-Time Dispatching of Large-Scale Ride-Sharing Systems: Integrating Optimization, Machine Learning, and Model Predictive Control](https://mlsanity.com/paper/2003.10942/) (2020)
- [Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems](https://mlsanity.com/paper/2412.15573/) (2024)
- [Data-Efficient Multi-Agent Spatial Planning with LLMs](https://mlsanity.com/paper/2502.18822/) (2025)
- [RideAgent: An LLM-Enhanced Optimization Framework for Automated Taxi Fleet Operations](https://mlsanity.com/paper/2505.06608/) (2025)
- [LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning](https://mlsanity.com/paper/2505.22695/) (2025)
- [RideGym: A Standardized Interface for Real-World Large-Scale Ride-Sharing System](https://mlsanity.com/paper/2607.10173/) (2026)
- [Hierarchical Optimization via LLM-Guided Objective Evolution for Mobility-on-Demand Systems](https://mlsanity.com/paper/2510.10644/) (2025)
- [ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch](https://mlsanity.com/paper/2606.18803/) (2026)

## Abstract (arXiv)

Ride-sharing, which allows multiple passengers with different origin-destination (OD) pairs to share a single vehicle, is a challenging operational problem, as it requires orders with different OD pairs to be efficiently bundled and assigned to vehicles under uncertain and varying scenarios. Although multi-agent reinforcement learning (MARL) solutions have achieved promising performance, they suffer from limited generalization (adapting to different environmental scenarios), low transferability (adapting to different platform objectives), and training difficulties in large-scale systems, such as the curse of dimensionality. Recently, motivated by the scaling of large language models (LLMs), several works have incorporated LLMs into ride-hailing systems, either by employing LLMs directly as decision-making agents or using them for automatic algorithm design. However, none of these approaches support vehicle sharing, which complicates the problem by expanding both the state and action spaces exponentially. Moreover, most of them require frequent LLM calls at inference time, making them infeasible for real-time deployment. To address these issues, we propose RideSkill, a hierarchical method for ride-sharing that leverages LLM-assisted automatic algorithmic design. RideSkill consists of a combiner that assigns appropriate skills to each vehicle from a learned skill repository, enabling adaptive dispatch under varying scenarios and objectives, and a repositioner that sequentially relocates idle vehicles to emerging regions, avoiding conflicts among vehicles. Crucially, the skill repository, combiner, and repositioner are all trained by an LLM-based automatic evolutionary method, eliminating the need for LLM calls during deployment and thus ensuring high real-time performance.