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ALIGN-HOLD replaces handcrafted heuristic rewards in ride-hailing hold control by learning an experience Reward Model directly from implicit preference pairs extracted across order trajectories, driver behaviors, and contemporaneous matching graphs. To overcome sparse behavioral signals and dynamic supply-demand confounding, the framework pairs balanced multi-view sampling with model-adaptive hard preference sampling to supply dense rewards and filter out low-identifiability interactions during simulator policy learning. In a 28-day production A/B test across 100,000 daily requests at DiDi Brazil, the approach significantly improved trip completion rates and driver earnings while slashing pre- and post-acceptance cancellation rates.
Ride-hailing dispatchers do not need brittle reward engineering: training a preference reward model directly on implicit multi-view passenger and driver trajectories outperforms heuristic-tuned hold policies in live, city-scale deployment.
Real-time hold control is a high-leverage mechanism in large-scale ride-hailing systems: by selectively deferring driver-order pairs, the platform can wait for better matching opportunities and improve end-to-end passenger-driver experience. Existing production systems such as EXHOLD learn bandit-based hold policies from handcrafted combinations of trip completion, cancellations, waiting time, and driver effort. However, designing such rewards becomes increasingly difficult as marketplace preferences are heterogeneous and observed passenger-driver behavior can be sparse, noisy, and affected by dynamic supply-demand conditions. We present ALIGN-HOLD, a production-scale experience alignment framework that learns hold policy from implicit marketplace preferences. ALIGN-HOLD constructs complementary preference pairs from order trajectories, driver trajectories, and contemporaneous local matching graphs, and trains an experience Reward Model (RM) using balanced multi-view sampling and model-adaptive hard preference sampling. During simulator-based policy learning, the frozen RM provides a dense, context-dependent reward and supports label-free filtering of low-identifiability interactions whose behavioral feedback is difficult to attribute to matching quality. We deploy ALIGN-HOLD on DiDi's ride-hailing platform and evaluate it in a 28-day randomized A/B experiment, covering approximately 100,000 passenger requests per day. Compared with the deployed production policy, ALIGN-HOLD achieves statistically significant improvements in trip completion rate and driver income, while significantly reducing passenger cancellations before and after driver acceptance. Complementary ablations, RM diagnostics, and behavioral analyses validate the contributions of the proposed components. ALIGN-HOLD has been fully ramped up and is currently serving DiDi's Brazil marketplace.
