Core Insights - The article provides a comprehensive analysis of the intersection of reinforcement learning (RL) and visual intelligence, focusing on the evolution of strategies and key research themes in visual reinforcement learning [5][17][25]. Group 1: Key Themes in Visual Reinforcement Learning - The article categorizes over 200 representative studies into four main pillars: multimodal large language models, visual generation, unified model frameworks, and visual-language-action models [5][17]. - Each pillar is examined for algorithm design, reward engineering, and benchmark progress, highlighting trends and open challenges in the field [5][17][25]. Group 2: Reinforcement Learning Techniques - Various reinforcement learning techniques are discussed, including Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), which are used to enhance stability and efficiency in training [15][16]. - The article emphasizes the importance of reward models, such as those based on human feedback and verifiable rewards, in guiding the training of visual reinforcement learning agents [10][12][21]. Group 3: Applications in Visual and Video Reasoning - The article outlines applications of reinforcement learning in visual reasoning tasks, including 2D and 3D perception, image reasoning, and video reasoning, showcasing how these methods improve task performance [18][19][20]. - Specific studies are highlighted that utilize reinforcement learning to enhance capabilities in complex visual tasks, such as object detection and spatial reasoning [18][19][20]. Group 4: Evaluation Metrics and Benchmarks - The article discusses the need for new evaluation metrics tailored to large model visual reinforcement learning, combining traditional metrics with preference-based assessments [31][35]. - It provides an overview of various benchmarks that support training and evaluation in the visual domain, emphasizing the role of human preference data in shaping reward models [40][41]. Group 5: Future Directions and Challenges - The article identifies key challenges in visual reinforcement learning, such as balancing depth and efficiency in reasoning processes, and suggests future research directions to address these issues [43][44]. - It highlights the importance of developing adaptive strategies and hierarchical reinforcement learning approaches to improve the performance of visual-language-action agents [43][44].

Core Insights - The article introduces ThinkAct, a dual-system framework designed to enhance the reasoning capabilities of multi-modal large language models (MLLMs) in physical environments by connecting high-level reasoning with low-level action execution [4][9][12] - ThinkAct aims to address the limitations of existing VLA models that struggle with long-term planning and adapting to complex tasks by utilizing reinforced visual latent planning [4][6][9] Group 1: Framework and Methodology - ThinkAct employs a structured approach to VLA reasoning tasks, where the model receives visual observations and textual instructions to predict actions, effectively linking abstract planning with low-level control [12][21] - The framework utilizes reinforcement learning to enhance the reasoning capabilities of MLLMs, encouraging them to generate low-level actions after reasoning through the task [13][19] - A novel action-aligned visual feedback mechanism is introduced to capture long-term goals and encourage visual associations during the planning process [14][18] Group 2: Performance Evaluation - ThinkAct demonstrates superior performance in various robotic operation tasks, achieving a top success rate of 84.4% on the LIBERO benchmark, outperforming other models like DiT-Policy and CoT-VLA [25][26] - In the SimplerEnv evaluation, ThinkAct outperformed baseline action models by significant margins, achieving overall scores of 71.5%, 65.1%, and 43.8% across different settings [25] - The framework also excels in embodied reasoning tasks, showing advantages in long-term and multi-step planning capabilities, as evidenced by its performance on EgoPlan-Bench2 and RoboVQA benchmarks [26][27] Group 3: Qualitative Insights - The article provides qualitative examples illustrating ThinkAct's reasoning process and execution in tasks, showcasing its ability to decompose instructions into meaningful sub-goals and visualize planning trajectories [30][31] - The framework's reinforcement learning adjustments significantly enhance its reasoning capabilities, allowing it to better understand tasks and environments compared to cold-start models [31][32] Group 4: Adaptability and Error Correction - ThinkAct demonstrates effective few-shot adaptation capabilities, successfully generalizing to unseen environments and new skills with minimal demonstration samples [35][37] - The framework's ability to detect execution errors and perform ego correction is highlighted, showcasing its structured reasoning to reconsider tasks and generate corrective plans when faced with failures [37][38]

Core Viewpoint - The article discusses the SEA framework (Synthetic Embedding for Enhanced Safety Alignment) developed by the team at Beihang University, which addresses the low-resource safety alignment challenges of multimodal large language models (MLLMs) by using synthetic embeddings instead of real multimodal data [1][2][3]. Summary by Sections Introduction - The SEA framework innovatively replaces real multimodal data with synthetic embeddings, providing a lightweight solution for the safe deployment of large models [1]. Challenges in MLLM Safety Alignment - MLLMs face three main challenges in safety alignment: 1. Reducing the cost of constructing multimodal safety alignment datasets [4]. 2. Overcoming the limitations of text alignment methods in non-text modal attack scenarios [5]. 3. Providing a universal safety alignment solution for emerging modalities [6]. SEA Framework Overview - SEA synthesizes embeddings from the representation space of modal encoders, allowing for cross-modal safety alignment using only text input, thus overcoming the high costs and strong modality dependencies of real data [6][8]. Data Preparation - The framework requires a text safety alignment dataset containing harmful instructions, which are used to optimize a set of embedding vectors [12]. Embedding Optimization - The optimization process aims to maximize the probability of the MLLM generating specified outputs based on the optimized embeddings, while keeping the MLLM parameters frozen [16][17]. Safety Alignment Implementation - To integrate the embedding vectors with the text dataset, specific prefixes are added to the text instructions, allowing for the construction of multimodal datasets for safety alignment training [19]. VA-SafetyBench: Safety Evaluation Benchmark - VA-SafetyBench is a safety evaluation benchmark for MLLMs that includes video and audio safety assessments, expanding upon existing image safety benchmarks [20][21]. Experimental Results - The SEA framework demonstrated effectiveness in reducing the success rate of multimodal attacks compared to traditional methods, particularly in complex attack scenarios involving images, videos, and audio [32][36]. Conclusion - The SEA framework shows promise as a solution for the safety alignment of emerging MLLMs, allowing for effective multimodal safety alignment using synthetic embeddings, which significantly reduces resource requirements [37].