Core Insights - The article discusses the challenges and potential of Vibe Coding in AI infrastructure development, highlighting its limitations in complex systems and proposing a document-driven approach to enhance its effectiveness [3][5][20]. Group 1: Challenges of Vibe Coding - Vibe Coding faces three main issues: context loss, decision deviation, and quality instability, primarily due to the lack of a structured decision management mechanism [4][5]. - The complexity of AI infrastructure, characterized by thousands of lines of code and numerous interrelated decision points, exacerbates these challenges [4][5]. Group 2: Document-Driven Vibe Coding Methodology - The document-driven approach aims to systematize key decisions during the design phase, significantly reducing complexity and improving code quality [6][20]. - By focusing on high-level design decisions, developers can leverage AI for detailed code implementation, achieving complex functionalities with minimal coding [7][20]. Group 3: Implementation in Agentic RL - The article presents a case study on optimizing GPU utilization in Agentic Reinforcement Learning (RL) systems, which face significant resource scheduling challenges [11][12]. - A proposed time-sharing reuse scheme dynamically allocates GPU resources, addressing the inefficiencies of existing solutions and improving overall system performance [14][15]. Group 4: Performance Validation - Experiments on a large-scale GPU cluster demonstrated that the time-sharing reuse scheme increased rollout throughput by 3.5 times compared to traditional methods, significantly enhancing task completion rates and reducing timeout occurrences [46][50]. - The analysis indicates that the additional system overhead introduced by the new scheme is minimal, validating its practical value in large-scale Agentic RL training [53][55]. Group 5: Team and Future Directions - The article concludes with an introduction to the ROCK & ROLL team, which focuses on advancing RL technologies and enhancing the practical application of large language models [57]. - The team emphasizes collaboration and open-source contributions to foster innovation in the RL community [58].

Core Viewpoint - The article discusses the current landscape of Reinforcement Learning (RL) training frameworks, highlighting the diversity and specific strengths and weaknesses of various open-source options, particularly focusing on the challenges of adapting these frameworks for multi-modal models in real-world environments [2][3]. Summary by Sections Overview of RL Frameworks - The open-source community has a wide variety of RL training frameworks, including established ones like openlhf, trl, unsloth, and verl, as well as newer entries like slime, AReaL, Rlinf, RL2, and ROLL [2]. Framework Selection Criteria - The author emphasizes the need for a community-active framework that requires minimal code modification for environmental adaptation, ultimately selecting AReaL due to its flexibility in handling multi-turn interactions [3]. GPU Management in RL Training - The article discusses the GPU orchestration challenges in RL training, noting that traditional frameworks often follow a synchronous training model, which can lead to inefficiencies and wasted resources [5][12]. Data Flow and Structure - The data flow in RL training frameworks is crucial, with verl using a specific data format called DataProto for efficient data transfer, although this can become a burden in agentic RL scenarios [10][11]. Asynchronous vs. Synchronous Training - Asynchronous RL training frameworks are highlighted for their efficiency, but they also introduce challenges such as data offset issues and higher GPU resource consumption compared to synchronous models [11][12]. Control Flow in RL Training - The control flow in RL training remains primarily on the training side, with the article explaining that the training process is similar to standard LLM training, differing mainly in the loss function used [15]. Weight Transfer Between Engines - The article details the complexities involved in transferring model weights from the training engine to the inference engine, particularly when the two engines have different model partitioning schemes [16][19]. Gaps in RL Training - Two significant gaps are identified: the need for on-policy data in RL training and the discrepancies in token distributions between rollout and prefill processes, which complicate the calculation of importance sampling [20][23]. Environment Adaptation and Reward Management - The article emphasizes the importance of environment adaptation and reward calculation in agentic RL training, noting that different frameworks handle these aspects differently, with AReaL and slime offering more flexible solutions [24][26]. Asynchronous Training Solutions - AReaL's asynchronous training approach is presented as a mature solution, utilizing a producer-consumer model to manage data flow efficiently [29][30]. Partial Rollout Management - The concept of partial rollout is introduced as a method to manage ongoing tasks during model weight updates, allowing for efficient training without interrupting the inference process [37][38]. Insights on RL Algorithms - The article concludes with reflections on RL algorithms, discussing the challenges of reward structuring and the potential benefits of staged training approaches [39][40]. Code Complexity and Usability - The author notes the complexity of the code in frameworks like AReaL and verl, suggesting that while they are well-engineered, they may pose a steep learning curve for new users [43][44].

Core Insights - The article discusses the launch of ROCK, a new open-source project by Alibaba that addresses the challenge of scaling AI training in real environments [2][5]. - ROCK, in conjunction with the existing ROLL framework, creates a complete training loop for AI agents, enabling developers to deploy standardized environments for training without the need for complex setups [3][4][5]. Group 1: AI Training Environment - The current evolution of large language models (LLMs) into Agentic models requires them to interact deeply with external environments, moving beyond mere text generation to executing actions [6][7]. - A stable and efficient training environment is crucial for the scaling potential of Agentic models, as it directly impacts the performance and learning capabilities of the AI [9][10]. - The performance bottleneck in training processes often stems from the limitations of the training environment, necessitating a dual approach to develop both high-performance RL frameworks and efficient environment management systems [10]. Group 2: ROLL Framework - ROLL is built on Ray and is designed specifically for large-scale reinforcement learning, covering the entire RL optimization process from small-scale research to production environments with billions of parameters [12]. - ROLL enhances training speed through asynchronous interactions and redundancy sampling, utilizing a simplified standard interface called GEM [13][14]. - The design of ROLL allows for quick adaptation to new applications, enabling seamless integration of various tasks from simple games to complex tool interactions [15]. Group 3: ROCK's Features - ROCK aims to facilitate the scaling of AI training by allowing concurrent processing of thousands of instances, addressing the resource limitations of traditional training environments [22][24]. - It provides a unified environment resource pool, enabling rapid deployment and management of training environments, significantly reducing setup time from days to minutes [25][26]. - ROCK offers unprecedented flexibility, allowing both homogeneous and heterogeneous environments to run simultaneously within the same cluster, enhancing the generalization capabilities of agents [27][28]. Group 4: Debugging and Stability - ROCK addresses the common issue of "black box" environments by providing developers with a comprehensive debugging interface, allowing for deep interaction with multiple remote sandboxes [30][33]. - The system is designed for enterprise-level stability, featuring fault isolation and precise resource scheduling to ensure high-quality data collection and model convergence [41][44]. - Quick state management ensures that any environment failures can be rapidly reset, maintaining the continuity of the training pipeline [45]. Group 5: ModelService Integration - ROCK introduces ModelService as an intermediary that decouples the agent's business logic from the training framework, allowing for smoother collaboration between the two [50][51]. - This architecture reduces maintenance complexity and enhances cost efficiency by concentrating GPU resources on centralized inference services while running large-scale environments on lower-cost CPU instances [57]. - The design promotes compatibility and flexibility, enabling support for custom agent logic while maintaining robust training capabilities [58].

Core Insights - The article discusses the application of Reinforcement Learning (RL) in the design of large language model systems and offers preliminary suggestions for future development [3] - It emphasizes the complexity of RL systems, particularly in their engineering and infrastructure requirements, and highlights the evolution from traditional RLHF systems to more advanced RL applications [4][24] Group 1: RL Theory and Engineering - The engineering demands of RL algorithms are multifaceted, focusing on the integration of large language models with RL systems [4] - The interaction between agents and their environments is crucial, with the environment defined as how the language model interacts with users or tools [7][8] - Reward functions are essential for evaluating actions, and advancements in reward modeling have significantly impacted the application of RL in language models [9][10] Group 2: Algorithmic Developments - The article outlines the evolution of algorithms such as PPO, GRPO, and DPO, noting their respective advantages and limitations in various applications [13][19] - The shift from human feedback to machine feedback in RL practices is highlighted, showcasing the need for more robust evaluation mechanisms [11][24] - The GRPO algorithm's unique approach to estimating advantages without relying on traditional critic models is discussed, emphasizing its application in inference-heavy scenarios [19] Group 3: Large-Scale RL Systems - The rapid advancements in RL applications are noted, with a transition from simple human alignment to more complex model intelligence objectives [24] - The challenges of integrating inference engines and dynamic weight updates in large-scale RL systems are outlined, emphasizing the need for efficient resource management [28][35] - Future developments in RL systems will require a focus on enhancing inference efficiency and flexibility, as well as building more sophisticated evaluation frameworks [41][58] Group 4: Open Source and Community Collaboration - The article mentions various open-source frameworks developed for RL, such as Open RLHF and VeRL, which aim to enhance community collaboration and resource sharing [50][56] - The importance of creating a vibrant ecosystem that balances performance and compatibility in RL systems is emphasized, encouraging industry participation in collaborative design efforts [58]

Core Viewpoint - The article discusses the transformative impact of generative AI on productivity and the evolution of e-commerce, particularly focusing on Alibaba's Taobao and its advancements in AI technology [2][6][11]. Group 1: AI Technology Evolution - The evolution of AI technology has accelerated, leading to the emergence of various models and applications, with a focus on multi-modal capabilities [3][11]. - Taobao has integrated AI deeply into its operations, upgrading its AIGX technology system to cover all necessary e-commerce scenarios [3][11]. - The introduction of generative AI is expected to bring a generational leap in productivity, with multi-modal intelligence becoming a core technology [11][12]. Group 2: Taobao's AI Innovations - Taobao launched RecGPT, a recommendation model with 100 billion parameters, enhancing the user experience by providing personalized recommendations [14][21]. - The generative recommendation algorithm can create new content based on user preferences, moving beyond traditional recommendation systems [16][20]. - The AI-driven video generation model, Taobao Star, automates the creation of promotional videos, significantly reducing content production costs for merchants [25][27]. Group 3: Open Source and Industry Impact - Taobao has open-sourced its reinforcement learning framework ROLL, aimed at improving user experience and enhancing model training efficiency [38][39]. - The company is gradually releasing its validated capabilities to the external market, fostering industry growth towards a "superintelligent" era [39][40]. - The rapid advancements in AI processing complexity and reduction in error rates suggest that narrow AGI could be achieved within 5-10 years [40].

Core Viewpoint - Reinforcement Learning (RL) is transitioning from a supportive technology to a core driver of model capabilities, focusing on multi-step, interactive agent training to achieve General Artificial Intelligence (AGI) [2][6]. Group 1: Modern RL Infrastructure Architecture - The core components of modern RL infrastructure include a Generator, which interacts with the environment to generate trajectories and calculate rewards, and a Trainer, which updates model parameters based on trajectory data [6][4]. - The generator-trainer architecture, combined with distributed coordination layers like Ray, forms the "gold standard" for RL systems [6][4]. Group 2: Primary Development - Primary Development frameworks serve as foundational frameworks for building RL training pipelines, providing core algorithm implementations and integration with underlying training/inference engines [8][7]. - TRL (Transformer Reinforcement Learning) is a user-friendly RL framework launched by Hugging Face, offering various algorithm supports [9][10]. - OpenRLHF, developed by a collaborative team including ByteDance and NetEase, aims to provide an efficient and scalable RLHF and Agentic RL framework [11][14]. - veRL, developed by Byte's Seed team, is one of the most comprehensive frameworks with extensive algorithm support [16][19]. - AReaL (Asynchronous Reinforcement Learning) is designed for large-scale, high-throughput RL training with a fully asynchronous architecture [20][21]. - NeMo-RL, launched by NVIDIA, integrates into its extensive NeMo ecosystem, focusing on production-level RL frameworks [24][28]. - ROLL, an Alibaba open-source framework, emphasizes asynchronous and Agentic capabilities for large-scale LLM RL [30][33]. - slime, developed by Tsinghua and Zhipu, is a lightweight framework focusing on seamless integration of SGLang with Megatron [34][36]. Group 3: Secondary Development - Secondary Development frameworks are built on primary frameworks, targeting specific downstream application scenarios like multi-modal, multi-agent, and GUI automation [44][3]. - Agentic RL frameworks, such as verl-agent, optimize for asynchronous rollout and training, addressing the core challenges of multi-round interactions with external environments [46][47]. - Multimodal RL frameworks, like VLM-R1 and EasyR1, focus on training visual-language reasoning models, addressing data processing and loss function design challenges [53][54]. - Multi-Agent RL frameworks, such as MARTI, integrate multi-agent reasoning and reinforcement learning for complex collaborative tasks [59][60]. Group 4: Summary and Trends - The RL infrastructure is evolving from a "workshop" model to a "standardized pipeline," with increasing modularity in framework design [65]. - Asynchronous architectures are becoming essential to address the computational asymmetry between rollout and training [66]. - The emergence of high-performance inference engines like vLLM and SGLang significantly accelerates the rollout process [66]. - The evolution from RLHF to Agentic RL reflects the growing complexity of tasks supported by new frameworks [66]. - Distributed training framework choices, such as Megatron-LM and DeepSpeed, are critical for large-scale model training [66]. - Scene-driven secondary development frameworks are addressing unique challenges in vertical domains [66]. - The importance of orchestrators for managing distributed components in RL systems is becoming widely recognized [66].

Group 1 - The article discusses the transition from Supervised Fine-Tuning (SFT) to Reinforcement Learning (RL) in model training paradigms, highlighting that RL is becoming increasingly critical for enhancing model capabilities [3][4][8] - RL algorithms are evolving with new methods such as GRPO, RLOO, and DAPO, focusing on improving stability and sample efficiency [4] - The RL training process consists of three main modules: Rollout (policy generation), Reward Evaluation, and Policy Update, each playing a vital role in the training framework [5][6][7] Group 2 - The design of RL training frameworks faces challenges in coordinating Rollout and training modules, especially with the increasing model scale and the need for distributed multi-GPU training [12][13] - There is a diversity of underlying training and inference frameworks, which complicates parameter synchronization and inference scheduling [14] - Performance optimization strategies include data parallelism, tensor parallelism, and pipeline parallelism, each with distinct advantages and limitations [22][24] Group 3 - The article outlines the importance of efficient data transfer mechanisms and parameter synchronization between training frameworks and inference engines, emphasizing the need for flexible communication strategies [32][39] - SLIME and ROLL frameworks are introduced, showcasing their approaches to managing data transfer and parameter synchronization effectively [42][46] - The integration of Ray for distributed computing is discussed, highlighting its role in managing resource allocation and communication in complex RL tasks [48][53] Group 4 - The article concludes with a comparison of various RL frameworks, such as SLIME, ROLL, and Verl, each catering to different needs and offering unique features for specific applications [61] - The rapid evolution of technology necessitates maintaining simplicity and high maintainability in framework design to adapt to new trends [58] - The article emphasizes the significance of open-source frameworks in advancing RL technology, particularly in the context of China's leading position in technical strength and understanding [60]

Core Insights - The article discusses the limitations of existing Mobile/APP Agents that primarily rely on action-level rewards, which restrict their adaptability in dynamic environments [1][2] - A new interactive reinforcement learning framework called Mobile-R1 is proposed, which incorporates task-level rewards to enhance agent adaptability and exploration capabilities [5][30] - The training process for Mobile-R1 consists of three stages: format fine-tuning, action-level training, and task-level training, which collectively improve the model's performance [6][31] Summary by Sections Existing Limitations - Current Mobile/APP Agents struggle with real-time adaptability due to their reliance on action-level rewards, making it difficult to handle changing mobile environments [1][2] - An example illustrates the failure of existing models in executing complex multi-step tasks [3] Proposed Solution - The collaboration between TaoTian Group's algorithm team and Future Life Lab introduces a multi-round, task-oriented learning approach that combines online learning and trajectory correction [4] - Mobile-R1 is designed to utilize task-level rewards, which are more effective in guiding agents through complex tasks [5] Training Methodology - The training process is divided into three stages: 1. **Format Fine-tuning**: Initial adjustments using supervised fine-tuning with high-quality trajectory data [16] 2. **Action-level Training**: Utilizes group relative policy optimization (GRPO) to evaluate action correctness with action-level rewards [17] 3. **Task-level Training**: Enhances model generalization and exploration through multi-step task-level training [18][20] Experimental Results - Mobile-R1 demonstrated superior performance across various benchmarks, achieving a task success rate of 49.40%, significantly higher than the best baseline model [26] - The results indicate that the three-stage training process effectively improves the model's robustness and adaptability, particularly in dynamic environments [29][30] - The article concludes that Mobile-R1's integration of interactive reinforcement learning and task-level rewards significantly enhances the capabilities of visual language model-based mobile agents [30][32]