Core Insights - Xiaomi's founder and CEO Lei Jun announced that multiple research achievements from the Xiaomi team have been selected for ICLR 2026, covering areas such as multimodal reasoning, reinforcement learning, GUI agents, end-to-end autonomous driving, and audio generation [1][3]. Group 1: Research Achievements - The research paper titled "Shuffle-R1: Efficient RL framework for Multimodal Large Language Models via Data-centric Dynamic Shuffle" addresses inefficiencies in existing reinforcement learning training processes, particularly issues like Advantage Collapsing and Rollout Silencing, which hinder long-term optimization capabilities [4]. - Shuffle-R1 proposes a streamlined reinforcement learning framework that significantly enhances training efficiency through two core designs: Pairwise Trajectory Sampling and Advantage-based Batch Shuffle, leading to improved gradient signal quality and increased exposure of valuable trajectories [4]. - Experimental results indicate that Shuffle-R1 consistently outperforms various reinforcement learning baselines with minimal computational overhead [4]. Group 2: Mobile Agents and GUI - The paper "MobileIPL: Enhancing Mobile Agents Thinking Process via Iterative Preference Learning" introduces a framework to improve the reasoning and planning capabilities of Mobile GUI Agents, addressing challenges such as the scarcity of high-quality CoaT trajectories and the limitations of existing self-training methods [7][8]. - MobileIPL employs Thinking-level DPO and Instruction Evolution to enhance process supervision and expand task distribution, resulting in state-of-the-art performance on mainstream GUI-Agent benchmarks [8][10]. Group 3: Language Models - "FutureMind: Equipping Small Language Models with Strategic Thinking-Pattern Priors via Adaptive Knowledge Distillation" presents a modular reasoning framework for small language models (SLMs) that enhances their performance in complex tasks without additional training or parameter increments [12][13]. - FutureMind extracts advanced cognitive abilities from large language models (LLMs) through adaptive knowledge distillation, creating a dynamic reasoning pipeline that significantly improves reasoning efficiency and retrieval accuracy [12][13]. Group 4: Multimodal Reasoning - The paper "ThinkOmni: Lifting Textual Reasoning to Omni-modal Scenarios via Guidance Decoding" proposes a framework that transfers mature textual reasoning capabilities to multimodal scenarios without the need for costly model fine-tuning [16][17]. - ThinkOmni includes components like LRM-as-a-Guide and Stepwise Contrastive Scaling, which balance perception and reasoning signals, demonstrating consistent performance improvements across multiple multimodal reasoning benchmarks [17]. Group 5: Audio Generation - "Flow2GAN: Hybrid Flow Matching and GAN with Multi-Resolution Network for Few-step High-Fidelity Audio Generation" introduces a two-stage audio generation framework that combines Flow Matching pre-training with lightweight GAN fine-tuning for efficient audio generation [23][24]. - The framework enhances audio modeling capabilities by addressing the unique properties of audio signals and demonstrates superior performance in generating high-fidelity audio with improved computational efficiency compared to existing methods [24].

Core Insights - The article discusses advancements in Vision-Language-Action (VLA) models for autonomous driving, highlighting a shift from traditional supervised learning methods to reinforcement learning (RL) approaches to enhance model generalization and reasoning capabilities [2]. Summary by Sections VLA + RL Research Overview - The article summarizes recent works in the VLA + RL domain, indicating a trend towards using RL to address limitations in previous models, particularly in terms of hallucination issues and the efficiency of continuous action space exploration [2]. Key Papers and Contributions - **MindDrive**: Introduces a framework that transforms action space into a discrete language decision space, achieving a driving score of 78.04 and a success rate of 55.09% on the Bench2Drive benchmark using a lightweight model [6]. - **WAM-Diff**: Proposes an end-to-end VLA framework that utilizes masked diffusion for trajectory optimization, achieving superior performance on the NAVSIM benchmark [7]. - **LCDrive**: Addresses temporal expression and latency issues in text chain reasoning by employing a latent chain-of-thought mechanism, demonstrating improved reasoning efficiency and trajectory quality [12]. - **Reasoning-VLA**: Develops a framework that enhances parallel trajectory generation through learnable action queries, achieving high performance across multiple datasets [13]. - **Alpamayo-R1**: Bridges reasoning and action prediction through a modular architecture and multi-stage training, improving generalization in long-tail scenarios [18]. - **AdaThinkDrive**: Introduces a dual-mode mechanism to balance decision accuracy and reasoning efficiency, achieving a PDMS score of 90.3 on the Navsim benchmark [20]. - **AutoDrive-R²**: Combines supervised fine-tuning and RL to enhance trajectory planning accuracy, achieving state-of-the-art performance with a significant reduction in error rates [25]. - **IRL-VLA**: Proposes a framework that avoids reliance on simulators by using a reward world model, achieving state-of-the-art performance on the NAVSIM v2 benchmark [31]. - **DriveAgent-R1**: Integrates active perception with hybrid thinking, achieving significant improvements in decision reliability and efficiency [32]. - **Drive-R1**: Connects reasoning and planning in VLMs, providing effective methods for integrating reasoning with motion planning [37]. - **ReCogDrive**: Merges cognitive reasoning with diffusion planners, achieving state-of-the-art performance while addressing the limitations of imitation learning [38].

Core Insights - The focus of academia and industry has shifted towards VLA (Vision-Language Action) in autonomous driving, which provides human-like reasoning capabilities for vehicle decision-making [1][4] - Traditional methods in perception and lane detection have matured, leading to decreased attention in these areas, while VLA is now a critical area for development among major autonomous driving companies [4][6] Summary by Sections Introduction to VLA - VLA is categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA, which are essential for improving the reliability and safety of autonomous driving [1][4] Course Overview - A comprehensive course on autonomous driving VLA has been designed, covering foundational principles to practical applications, including cutting-edge algorithms like CoT, MoE, RAG, and reinforcement learning [6][12] Course Structure - The course consists of six chapters, starting with an introduction to VLA algorithms, followed by foundational algorithms, VLM as an interpreter, modular and integrated VLA, reasoning-enhanced VLA, and a final project [12][20] Chapter Highlights - Chapter 1 provides an overview of VLA algorithms and their development history, along with benchmarks and evaluation metrics [13] - Chapter 2 focuses on the foundational knowledge of Vision, Language, and Action modules, including the deployment of large models [14] - Chapter 3 discusses VLM's role as an interpreter in autonomous driving, covering classic and recent algorithms [15] - Chapter 4 delves into modular and integrated VLA, emphasizing the evolution of language models in planning and control [16] - Chapter 5 explores reasoning-enhanced VLA, introducing new modules for decision-making and action generation [17][19] Learning Outcomes - The course aims to deepen understanding of VLA's current advancements, core algorithms, and applications in projects, benefiting participants in internships and job placements [24]

Core Viewpoint - The focus of academia and industry after end-to-end systems is on VLA (Vision-Language-Action), which provides human-like reasoning capabilities for safer and more reliable autonomous driving [1][4]. Summary by Sections Introduction to Autonomous Driving VLA - VLA is categorized into modular VLA, integrated VLA, and reasoning-enhanced VLA, which are essential for advancing autonomous driving technology [1][4]. Technical Maturity and Employment Demand - The demand for autonomous driving VLA solutions is high among major companies, prompting them to invest in self-research and development [4]. Course Overview - A comprehensive learning roadmap for autonomous driving VLA has been designed, covering principles to practical applications [4][6]. Core Content of Autonomous Driving VLA - Key topics include visual perception, large language models, action modeling, model deployment, and dataset creation, with cutting-edge algorithms like CoT, MoE, RAG, and reinforcement learning [6]. Course Collaboration - The course is developed in collaboration with Tsinghua University's research team, featuring detailed explanations of algorithms and practical assignments [6]. Course Structure - The course consists of six chapters, each focusing on different aspects of VLA, including algorithm introduction, foundational algorithms, VLM as an interpreter, modular and integrated VLA, reasoning-enhanced VLA, and a final project [12][20]. Chapter Details - Chapter 1 covers the concept and history of VLA algorithms, including benchmarks and evaluation metrics [13]. - Chapter 2 focuses on foundational algorithms related to Vision, Language, and Action, along with model deployment [14]. - Chapter 3 discusses VLM's role as an interpreter in autonomous driving, highlighting key algorithms [15]. - Chapter 4 delves into modular and integrated VLA, emphasizing the evolution of language models in planning [16]. - Chapter 5 explores reasoning-enhanced VLA, introducing new modules for decision-making and action output [17]. - Chapter 6 involves a hands-on project where participants build and fine-tune their models [20]. Learning Outcomes - The course aims to deepen understanding of VLA's current advancements and core algorithms, equipping participants with practical skills for future research and applications in the autonomous driving sector [22][26]. Course Schedule - The course is set to begin on October 20, with a structured timeline for each chapter's release [23]. Prerequisites - Participants are expected to have a foundational knowledge of autonomous driving, large models, reinforcement learning, and programming skills in Python and PyTorch [26].

Core Insights - The article discusses the advancements in Vision-Language-Action (VLA) models for autonomous driving, highlighting the integration of navigation and reinforcement learning to enhance reasoning capabilities beyond visual range [2][3][6]. Group 1: NavigScene - NavigScene is introduced as a novel auxiliary dataset that pairs local multi-view sensor inputs with global natural language navigation guidance, addressing the critical gap between local perception and global navigation context in autonomous driving [6]. - Three complementary paradigms are implemented in NavigScene: navigation-guided reasoning, navigation-guided preference optimization, and navigation-guided VLA models, enhancing the reasoning and generalization capabilities of autonomous driving systems [6]. - Comprehensive experiments demonstrate significant performance improvements in perception, prediction, and planning tasks by integrating global navigation knowledge into autonomous driving systems [6]. Group 2: AutoVLA - AutoVLA is proposed as an end-to-end autonomous driving framework that integrates physical action tokens with a pre-trained VLM backbone, enabling direct policy learning and semantic reasoning from raw visual observations and language instructions [12]. - A reinforcement learning-based post-training method using Group Relative Policy Optimization (GRPO) is introduced to achieve adaptive reasoning and further enhance model performance in end-to-end driving tasks [12]. - AutoVLA achieves competitive performance across multiple autonomous driving benchmarks, including open-loop and closed-loop tests [12]. Group 3: ReCogDrive - ReCogDrive is presented as an end-to-end autonomous driving system that integrates VLM with a diffusion planner, employing a three-stage training paradigm to address performance drops in rare and long-tail scenarios [13][16]. - The first stage involves fine-tuning the VLM on a large-scale driving Q&A dataset to mitigate domain gaps between general content and real-world driving scenarios [16]. - The method achieves a state-of-the-art PDMS score of 89.6 on the NAVSIM benchmark, highlighting its effectiveness and feasibility [16]. Group 4: Impromptu VLA - Impromptu VLA introduces a large-scale, richly annotated dataset aimed at addressing the limitations of existing benchmarks in autonomous driving VLA models [22]. - The dataset is designed to enhance the performance of VLA models in unstructured extreme scenarios, demonstrating significant improvements in established benchmarks [22]. - Experiments show that training with the Impromptu VLA dataset leads to notable performance enhancements in closed-loop NeuroNCAP scores and collision rates [22]. Group 5: DriveMoE - DriveMoE is a novel end-to-end autonomous driving framework that incorporates a mixture-of-experts (MoE) architecture to effectively handle multi-view sensor data and complex driving scenarios [28]. - The framework features scene-specific visual MoE and skill-specific action MoE, addressing the challenges of multi-view redundancy and skill specialization [28]. - DriveMoE achieves state-of-the-art performance in closed-loop evaluations on the Bench2Drive benchmark, demonstrating the effectiveness of combining visual and action MoE in autonomous driving tasks [28].

Core Insights - The article discusses the rapid advancements in end-to-end autonomous driving, particularly focusing on Vision-Language-Action (VLA) models and their applications in the industry [2][3]. Group 1: VLA Model Developments - The introduction of AutoVLA, a new VLA model that integrates reasoning and action generation for end-to-end autonomous driving, shows promising results in semantic reasoning and trajectory planning [3][4]. - ReCogDrive, another VLA model, addresses performance issues in rare and long-tail scenarios by utilizing a three-stage training framework that combines visual language models with diffusion planners [7][9]. - Impromptu VLA introduces a dataset aimed at improving VLA models' performance in unstructured extreme conditions, demonstrating significant performance improvements in established benchmarks [14][24]. Group 2: Experimental Results - AutoVLA achieved competitive performance metrics in various scenarios, with the best-of-N method reaching a PDMS score of 92.12, indicating its effectiveness in planning and execution [5]. - ReCogDrive set a new state-of-the-art PDMS score of 89.6 on the NAVSIM benchmark, showcasing its robustness and safety in driving trajectories [9][10]. - The OpenDriveVLA model demonstrated superior results in open-loop trajectory planning and driving-related question-answering tasks, outperforming previous methods on the nuScenes dataset [28][32]. Group 3: Industry Trends - The article highlights a trend among major automotive manufacturers, such as Li Auto, Xiaomi, and XPeng, to invest heavily in VLA model research and development, indicating a competitive landscape in autonomous driving technology [2][3]. - The integration of large language models (LLMs) with VLA frameworks is becoming a focal point for enhancing decision-making capabilities in autonomous vehicles, as seen in models like ORION and VLM-RL [33][39].