RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing

• URL: http://arxiv.org/abs/2512.13727v1
• Date: Sat, 13 Dec 2025 20:49:15 GMT
• Title: RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing
• Authors: Yuhan Tang, Kangxin Cui, Jung Ho Park, Yibo Zhao, Xuan Jiang, Haoze He, Dingyi Zhuang, Shenhao Wang, Jiangbo Yu, Haris Koutsopoulos, Jinhua Zhao,
• Abstract summary: Regime-of-Experts (RAST-MoE) formalizes adaptive delayed matching as a regime-aware MDP equipped with a self-attention MoE encoder.<n>A physics-informed congestion preserves realistic density-speed feedback, enabling millions of efficient rollouts, while an adaptive reward scheme guards against pathological strategies.
• Score: 11.542008509248836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply-demand conditions. Adaptive delayed matching creates a trade-off between matching and pickup delays by deciding whether to assign drivers immediately or batch requests. Since outcomes accumulate over long horizons with stochastic dynamics, reinforcement learning (RL) is a suitable framework. However, existing approaches often oversimplify traffic dynamics or use shallow encoders that miss complex spatiotemporal patterns. We introduce the Regime-Aware Spatio-Temporal Mixture-of-Experts (RAST-MoE), which formalizes adaptive delayed matching as a regime-aware MDP equipped with a self-attention MoE encoder. Unlike monolithic networks, our experts specialize automatically, improving representation capacity while maintaining computational efficiency. A physics-informed congestion surrogate preserves realistic density-speed feedback, enabling millions of efficient rollouts, while an adaptive reward scheme guards against pathological strategies. With only 12M parameters, our framework outperforms strong baselines. On real-world Uber trajectory data (San Francisco), it improves total reward by over 13%, reducing average matching and pickup delays by 10% and 15% respectively. It demonstrates robustness across unseen demand regimes and stable training. These findings highlight the potential of MoE-enhanced RL for large-scale decision-making with complex spatiotemporal dynamics.

Related papers

• Each Prompt Matters: Scaling Reinforcement Learning Without Wasting Rollouts on Hundred-Billion-Scale MoE [16.58714489761542]
  We present CompassMax-V3-Thinking, a hundred-billion-scale MoE reasoning model trained with a new RL framework built on one principle: each prompt must matter.<n>To overcome these challenges, we introduce several unified innovations.<n>The resulting model delivers strong performance across both internal and public evaluations.
  arXiv  Detail & Related papers  (2025-12-08T16:57:43Z)
• Taming the Long-Tail: Efficient Reasoning RL Training with Adaptive Drafter [52.111923076688505]
  Training Large Language Models (LLMs) with strong reasoning capabilities marks a significant milestone, unlocking new frontiers in complex problem-solving.<n>We propose TLT, a system that accelerates reasoning RL training losslessly by integrating adaptive speculative decoding.
  arXiv  Detail & Related papers  (2025-11-20T18:59:25Z)
• Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail [85.47497935739936]
  Alpamayo-R1 (AR1) is a vision-language-action model that integrates Chain of Causation reasoning with trajectory planning.<n>We show AR1 achieves 12% improvement in planning accuracy on challenging cases compared to a trajectory-only baseline.<n>We plan to release AR1 models and a subset of the CoC in a future update.
  arXiv  Detail & Related papers  (2025-10-30T01:25:34Z)
• MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs [17.827406818899536]
  MoE-Prism is a model-system co-design that transforms rigid MoE models into elastic services.<n>Our evaluation shows that MoE-Prismprovides over 4 times more distinct, stable operating points than the baseline.<n>This allows an AI service to dynamically improve throughput by up to 19.9% under a strict budget or reduce latency by up to 10.36% under limited resources.
  arXiv  Detail & Related papers  (2025-10-22T08:40:01Z)
• STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning [54.28691219536054]
  We introduce STARec, a slow-thinking augmented agent framework that endows recommender systems with autonomous deliberative reasoning capabilities.<n>We develop anchored reinforcement training - a two-stage paradigm combining structured knowledge distillation from advanced reasoning models with preference-aligned reward shaping.<n>Experiments on MovieLens 1M and Amazon CDs benchmarks demonstrate that STARec achieves substantial performance gains compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-08-26T08:47:58Z)
• CSGO: Generalized Optimization for Cold Start in Wireless Collaborative Edge LLM Systems [62.24576366776727]
  We propose a latency-aware scheduling framework to minimize total inference latency.<n>We show that the proposed method significantly reduces cold-start latency compared to baseline strategies.
  arXiv  Detail & Related papers  (2025-08-15T07:49:22Z)
• WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training [64.0932926819307]
  We present Warmup-Stable and Merge (WSM), a framework that establishes a formal connection between learning rate decay and model merging.<n>WSM provides a unified theoretical foundation for emulating various decay strategies.<n>Our framework consistently outperforms the widely-adopted Warmup-Stable-Decay (WSD) approach across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-07-23T16:02:06Z)
• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting [52.6508222408558]
  We introduce Elucidated Rolling Diffusion Models (ERDM)<n>ERDM is the first framework to unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM)<n>On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5circ resolution, ERDM consistently outperforms key diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-06-24T21:44:31Z)
• When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL [37.58940726230092]
  Reinforcement learning (RL) excels in optimizing policies for discrete-time Markov decision processes (MDP) We formalize an RL framework, Time-adaptive Control & Sensing (TaCoS), that tackles this challenge. We demonstrate that state-of-the-art RL algorithms trained on TaCoS drastically reduce the interaction amount over their discrete-time counterpart.
  arXiv  Detail & Related papers  (2024-06-03T09:57:18Z)
• Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing [11.403989519949173]
  This work focuses on the timeliness of computational-intensive updates, measured by Age-ofInformation (AoI) We study how to jointly optimize the task updating and offloading policies for AoI with fractional form. Experimental results show that our proposed algorithms reduce the average AoI by up to 57.6% compared with several non-fractional benchmarks.
  arXiv  Detail & Related papers  (2023-12-16T11:13:40Z)
• DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks [1.3342521220589318]
  A drift magnitude threshold should be defined to design change detection methods to identify drifts. We propose a dynamic drift-adaptive Long Short-Term Memory (DA-LSTM) framework that can improve the performance of load forecasting models.
  arXiv  Detail & Related papers  (2023-05-15T16:26:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.