Core Insights - The article discusses the challenges of offline reinforcement learning (RL), particularly the fixed and variable quality of training data, and highlights the limitations of existing Transformer-based methods like Decision Transformer (DT) that treat entire trajectories as learning units, which can dilute effective actions within low-return trajectories [2][8][9]. Group 1: Pain Points - Offline RL faces the issue of fixed data, unlike online RL, which can continuously learn through trial and error. Current Transformer-based methods primarily model conditional sequences based on final returns, leading to a lack of granularity in learning [8][9]. - The existing approaches often fail to address the quality differences within trajectories, making it difficult for models to extract optimal segments from mediocre strategies [13][15]. Group 2: Core Solution - The PRGS (Peak-Return Greedy Slicing) framework is introduced as a data processing and inference enhancement tool for Transformer-based offline RL, consisting of three interconnected modules: return estimation, greedy slicing, and adaptive history truncation [10][11]. - The first module, MMD-based Return Estimator, uses a distribution perspective to provide a more optimistic return estimate by characterizing the potential return distribution of state-action pairs [16]. - The second module, Greedy Subtrajectory Slicing, recursively slices trajectories around peak returns, allowing the model to focus on higher-quality sub-trajectories during training [17][18]. - The third module, Adaptive History Truncation, addresses the issue of low-quality historical actions interfering with current decisions by allowing the model to discard irrelevant historical context [19]. Group 3: Experimental Results - The research team tested PRGS across various benchmarks, including D4RL, BabyAI, and AuctionNet, achieving state-of-the-art (SOTA) performance, particularly in complex scenarios [20][21]. - In the D4RL scenario, PRGS demonstrated remarkable performance, especially in the Maze2D-Large task, where the DT-PRGS scored 127.5 compared to less than 30 for the original DT [22]. - The visual results in maze tasks showed that PRGS effectively extracted sub-trajectories that covered the optimal path to the goal, eliminating ineffective explorations [24]. - In real-world applications, PRGS also showed significant profit improvements in the AuctionNet dataset compared to behavior cloning (BC) [25]. Group 4: Summary and Outlook - The success of PRGS emphasizes that in offline reinforcement learning, data must not only be abundant but also precise. The combination of MMD estimators, greedy slicing, and adaptive truncation enables Transformers to effectively filter valuable information [28].

Core Insights - The article discusses the potential of a new framework called SIHD (Structural Information-based Hierarchical Diffusion) for offline reinforcement learning, which adapts to various tasks by analyzing embedded structural information in offline trajectories [2][3][23]. Research Background and Motivation - Offline reinforcement learning aims to train effective policies using fixed historical datasets without new interactions with the environment. The introduction of diffusion models helps mitigate extrapolation errors caused by out-of-distribution states and actions [3][4]. - Current methods face limitations due to fixed hierarchical structures and single time scales, which hinder adaptability to different task complexities and decision-making flexibility [5][6]. SIHD Framework Core Design - SIHD innovates in three areas: hierarchical construction, conditional diffusion, and regularization exploration [5]. - The framework's hierarchical construction is adaptive, allowing the data's inherent structure to dictate the hierarchy [7][9]. - The conditional diffusion model uses structural information gain as a guiding signal, enhancing stability and robustness compared to traditional methods reliant on sparse reward signals [10][11]. - A structural entropy regularizer is introduced to encourage exploration and mitigate extrapolation errors, balancing exploration and exploitation in the training objective [12][13]. Experimental Results and Analysis - SIHD was evaluated on the D4RL benchmark, demonstrating superior performance in standard offline RL tasks and long-horizon navigation tasks [14][15]. - In Gym-MuJoCo tasks, SIHD achieved optimal average returns across various data quality levels, outperforming advanced hierarchical baselines with average improvements of 3.8% and 3.9% in medium-quality datasets [16][17][18]. - In long-horizon navigation tasks, SIHD showed significant advantages, particularly in sparse reward scenarios, with notable performance improvements in Maze2D and AntMaze tasks [19][20][22]. - Ablation studies confirmed the necessity of SIHD's components, especially the adaptive multi-scale hierarchy, which is crucial for performance in long-horizon tasks [21][22]. Conclusion - The SIHD framework successfully constructs an adaptive multi-scale hierarchical diffusion model, overcoming rigid limitations of existing methods and significantly enhancing offline policy learning performance, generalization, and robustness [23]. Future research may explore more refined sub-goal conditional strategies and extend SIHD's concepts to broader diffusion-based generative models [23].

Core Viewpoint - The article discusses the introduction of a new training paradigm called Semi-online Reinforcement Learning (Semi-online RL) by Zhejiang University and Tongyi Laboratory's Mobile-Agent team, which enhances the performance of models in dynamic multi-turn tasks without relying on real environment interactions [1][2][4]. Group 1: Methodology - The Semi-online RL framework combines the stability of offline training with the long-term optimization capabilities of online learning, significantly improving model performance in dynamic tasks [2][10]. - The framework utilizes offline data to simulate online interactions, allowing the model to experience contextual changes from its own actions during training [12][15]. - A patching mechanism is introduced to adaptively correct sampling biases when the model deviates from expert trajectories, enhancing the learning process [17][19]. Group 2: Key Technologies - The Semi-online RL framework consists of three core technologies: 1. Semi-online mechanism that simulates online interactions using offline data [12]. 2. Patching Module that self-adaptively repairs sampling biases [17]. 3. Long-term reward modeling that estimates advantages from step-level to trajectory-level [20]. Group 3: Evaluation and Results - The new evaluation metric SOP (Semi-online Performance) is proposed to better reflect the model's performance in multi-turn tasks, aligning closely with real online performance [22][23]. - Experimental results show that the UI-S1-7B model outperforms baseline models, achieving a task success rate of 34.0% in the AndroidWorld task, closely approaching the performance of top proprietary models [25][26]. - The model maintains a +7.1% gain in single-turn tasks, indicating that the semi-online training does not sacrifice local accuracy while optimizing for long-term performance [28]. Group 4: Component Analysis - The patching mechanism significantly enhances data utilization and maintains training stability, allowing for effective error correction and promoting policy diversity [30][37]. - Ablation studies confirm that the combination of trajectory-level and step-level advantage functions, along with multi-frame historical observations, positively impacts the model's decision-making capabilities in complex GUI interactions [44].

Core Insights - The article discusses the development of a new reinforcement learning framework called Chunked RL, specifically designed for fine-tuning Vision-Language-Action (VLA) models, which show great potential in real-world robotic control [4][8]. - The proposed CO-RFT algorithm demonstrates significant improvements over traditional supervised fine-tuning methods, achieving a 57% increase in success rate and a 22.3% reduction in cycle time in real-world environments [4][29]. Section Summaries Introduction - VLA models integrate perception and language understanding for embodied control, showing promise in developing general strategies for real-world robotic control [6]. - The challenges faced in fine-tuning VLA models primarily stem from the dependency on the quality and quantity of task-specific data, which limits generalization to out-of-distribution (OOD) scenarios [6][7]. Methodology - The article introduces Chunked RL, a novel reinforcement learning framework that incorporates action chunking to enhance sample efficiency and stability, particularly suited for VLA models [8][12]. - The CO-RFT algorithm consists of two phases: imitation learning for initializing the backbone network and policy, followed by offline RL with action chunking to optimize the pre-trained policy [16][18]. Experimental Analysis - The experiments were conducted on a robotic platform with six dexterous manipulation tasks, evaluating the performance of the CO-RFT algorithm against traditional methods [20][23]. - Results indicate that CO-RFT significantly outperforms supervised fine-tuning (SFT), achieving a 57% increase in success rate and a 22.3% decrease in average cycle time across various tasks [29][30]. Position Generalization - CO-RFT exhibits strong position generalization capabilities, achieving a 44.3% success rate in previously unseen locations, outperforming SFT by 38% in OOD scenarios [4][29]. Importance of Data Diversity - Data diversity plays a crucial role in the performance of CO-RFT, with models trained on diverse datasets showing significantly better generalization capabilities compared to those trained on fixed datasets [32][33].