RL

Introduction Figure from CORAL: Autonomous Multi-Agent Evolution for Open-Ended Discovery Open-ended discovery—the search for novel, high-quality solutions in domains where the solution space lacks clear structure and evaluation may be expensive or sparse—remains one of the hardest challenges in automated scientific reasoning. Unlike constrained optimization, where gradients or convexity guide the search, open-ended problems demand sustained exploration, accumulation of partial insights, and the ability to redirect effort when progress stalls. Mathematical conjecture proving, systems-level code optimization, and combinatorial design all fall squarely in this category. ...

中文版本：阅读中文版 Introduction The integration of reinforcement learning into end-to-end autonomous driving systems has emerged as a promising direction for improving trajectory planning beyond what supervised learning alone can achieve. However, the direct application of standard RL algorithms to driving tasks faces core challenges: the sim-to-real gap in log-replay environments, the computational bottleneck of online simulation, and the difficulty of defining dense reward signals for continuous trajectory generation. ...

中文版本：阅读中文版 Introduction Figure from Alpamayo-R1: Bridging Reasoning and Action Prediction for Autonomous Driving End-to-end autonomous driving has made significant progress in recent years, yet deploying Vision-Language-Action (VLA) models in real-world driving scenarios remains challenging. The basic difficulties are fourfold. First, multi-frame temporal understanding requires the model to extract decision-relevant changes from highly redundant consecutive observations, rather than merely processing static snapshots. Second, driving decisions must be causal: the model must model why a particular action is taken, not just learn statistical correlations between situations and actions. Third, predicted trajectories must satisfy kinematic and dynamic constraints while remaining multi-modal and efficient enough for real-time inference. Fourth, the reasoning process must be tightly aligned with action output—reasoning should not be a post-hoc rationalization but must be verifiable by and constrained by the actual actions taken. ...

中文版本：阅读中文版 1. Why End-to-End Driving Needs Reinforcement Learning Figure from AlphaDrive: GRPO-based RL for Autonomous Driving Supervised learning—whether through imitation learning or behavior cloning—can only take an autonomous driving system so far. The core limitation is distributional: the training data is drawn from expert demonstrations, and any distributional shift between training and deployment leads to compounding errors. More critically, supervised objectives are misaligned with the true goal of driving. Minimizing the L2 distance to a ground-truth trajectory penalizes safe deviations as harshly as dangerous ones, and provides no mechanism for the model to discover better trajectories than those in the dataset. ...

中文版本：阅读中文版 Figure from DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving Autoregressive (AR) trajectory generation — predicting driving trajectories as sequences of discrete tokens, much like language models predict text — has emerged as a powerful paradigm for end-to-end autonomous driving. But how do we turn continuous trajectories into discrete tokens? How do we ensure the tokenized representation preserves enough fidelity for planning? And how does the AR paradigm combine with diffusion and reinforcement learning to produce state-of-the-art results? This article walks through the complete pipeline, from tokenization theory to RL post-training. ...