Core Viewpoint - The article reflects on the evolution of the AI drawing community, particularly focusing on the transition from the early days of Stable Diffusion (SD) to the current state marked by the launch of liblib 2.0, indicating a significant shift in the landscape of AI tools and user engagement [2][55]. Group 1: Historical Context - The article reminisces about the peak of the SD open-source community, highlighting its rapid growth and the excitement it generated among users [11][31]. - It mentions the initial struggles and learning curves faced by users in understanding complex parameters and prompts necessary for generating images [50][51]. - The community was characterized by a sense of exploration and innovation, with users actively engaging in discussions and sharing techniques [47][41]. Group 2: Transition to Liblib 2.0 - Liblib has announced an upgrade to version 2.0, introducing a new brand, logo, interface, and features aimed at simplifying user experience and expanding its user base [3][67]. - The upgrade signifies a shift towards a more integrated platform that combines various AI drawing and video models, aiming to lower the entry barrier for new users [60][65]. - The article suggests that this transition is a natural progression in the industry, akin to technological advancements that replace older methods [56][57]. Group 3: Community and User Engagement - The article notes a decline in user engagement and interest in the original SD models, as newer, simpler tools have emerged that cater to a broader audience [9][54]. - Despite the changes, the community remains vibrant, with a focus on creativity and the enduring presence of talented creators [75][76]. - The narrative emphasizes that while tools may evolve or disappear, the essence of creativity and the community's spirit will persist [75][76].

闻乐 发自 凹非寺 量子位 | 公众号 QbitAI 距第二篇研究仅过去三天，Thingking Machines发布了第三篇研究博客。 核心作者是OpenAI联创之一 John Schulman 。 Thingking Machines创始人、OpenAI前CTO Mira Murati继续转发站台。 第三篇研究是关于 LoRA参数的高效微调方法 ，题目为《LoRA Without Regret》，探究了LoRA匹配全量微调（FullFT）效率的条件，还 给出了大幅降低调参难度的简化方案。 当前主流大模型动辄万亿参数，预训练数据达数十万亿token，但下游任务往往只需要小数据集、聚焦特定领域。 用FullFT更新所有参数，资源浪费严重。 而LoRA作为参数高效微调（PEFT）的核心方法，通过低秩矩阵A和B（总参数远少于原权重）捕捉微调信息，却始终面临一个争议： 它真的 能追上FullFT的性能吗？ John Schulman和Thingking Machines团队给出了肯定答案：只要抓准关键细节，LoRA不仅能和FullFT拥有相同的样本效率，还能达到一 样的最终性能。 下面具体来看。 LoRA最优学习率 ...

Core Insights - The article emphasizes the advantages of LoRA (Low-Rank Adaptation) over Full Fine-tuning (FullFT) in terms of cost-effectiveness and performance in various training scenarios [2][7][18]. Group 1: Importance of LoRA - LoRA is a popular parameter-efficient fine-tuning method that updates a low-dimensional adapter instead of the entire model weights, leading to lower memory requirements and faster loading [11][13]. - The research indicates that LoRA can achieve performance comparable to FullFT in small to medium-sized datasets, while it may struggle in large datasets due to capacity limitations [14][22]. Group 2: Key Findings - The study found that LoRA's performance is closely tied to the training conditions, including the size of the training dataset and the rank of the LoRA parameters [16][25]. - In reinforcement learning tasks, even with a very low rank (rank=1), LoRA can perform similarly to FullFT, indicating that reinforcement learning has lower capacity demands [29]. Group 3: Experimental Methodology - The research utilized models like LLaMA 3 and Qwen3, adjusting LoRA ranks from 1 to 512 and scanning learning rates to find optimal training conditions [20][21]. - Results showed that high-rank LoRA performed almost identically to FullFT in certain datasets, but performance varied across different tasks due to training dynamics [22][24]. Group 4: Practical Implications - LoRA's optimal learning rate is typically about 10 times that of FullFT, allowing it to accept higher learning rates under the same conditions [35]. - The study suggests that applying LoRA across all layers, especially MLP and MoE layers, is crucial for achieving performance close to FullFT [37].

Core Viewpoint - The article introduces CoTo, a progressive training strategy designed to enhance the robustness and effectiveness of Low-Rank Adaptation (LoRA) models, addressing issues such as training instability and performance drop after pruning [1][4][23]. Summary by Sections Conventional LoRA Training Issues - LoRA faces challenges including "lazy training," where optimization gets stuck near suboptimal solutions, limiting generalization [7] - There is a hierarchical imbalance in training, with gradient updates concentrated on top layers, leading to undertraining of lower layers [7] - These issues complicate downstream operations like model fusion and pruning, often resulting in unsatisfactory outcomes [7] CoTo Strategy - CoTo employs a simple yet effective progressive activation strategy, initially deactivating a portion of LoRA adapters to encourage uniform gradient flow across all layers [5][8] - The activation probability of adapters is gradually increased during training, returning to standard fine-tuning mode in later stages [8] Experimental Results - CoTo significantly improves the fusion and pruning capabilities of LoRA models, enhancing single-task generalization performance and training efficiency [12][23] - In linear interpolation tasks, CoTo models maintain smooth performance transitions, unlike standard LoRA, which experiences sharp declines [13] - CoTo outperforms standard LoRA in both structured and unstructured pruning scenarios, demonstrating enhanced fault tolerance [17] Performance and Efficiency Improvements - CoTo consistently boosts performance across various benchmarks, including visual and language tasks, and achieves over 24% training acceleration when applied to HiRA [24][23] Ablation Studies - Rigorous ablation studies validate the design choices of CoTo and provide insights into effective regularization of LoRA [21] Conclusion - CoTo effectively resolves hierarchical imbalance and lazy optimization issues in LoRA training, enhancing model robustness and simplifying downstream operations like fusion and pruning [23]

Core Viewpoint - The article discusses the limitations of current multimodal large model (MLLM) fine-tuning methods, which often replicate strategies from unimodal language models without considering the unique characteristics of multimodal learning [2][9][23]. Summary by Sections Introduction to MLLMs - Recent advancements in MLLMs have been significant in tasks involving visual-language and audio-language [2]. - Current fine-tuning methods primarily adapt strategies from unimodal language models, such as LoRA, which may not be suitable for multimodal contexts [2][8]. Limitations of Current Fine-Tuning Methods - Many efficient multimodal fine-tuning methods overlook the essential differences between modalities, leading to inadequate utilization of multimodal information [9][11]. - The article emphasizes the need for both unimodal adaptation and cross-modal adaptation in effective multimodal fine-tuning [9][12]. Introduction of MokA Method - The research team proposes a new method called MokA (Multimodal low-rank Adaptation), which balances the independent modeling of unimodal information and the interaction modeling between modalities [3][12][23]. - MokA retains the efficiency of LoRA while redefining the roles of projection matrices in a multimodal context [14][23]. Key Components of MokA - MokA includes three critical modules: 1. **Modality-specific A matrix**: Ensures independent modeling of unimodal information [15]. 2. **Cross-modal attention mechanism**: Enhances interaction between different modalities during instruction tuning [16]. 3. **Shared B matrix**: Facilitates implicit cross-modal alignment by projecting modalities into a shared space [17]. Experimental Results - MokA was evaluated across three representative multimodal task scenarios: audio-visual-text, visual-text, and speech-text [19]. - The method demonstrated significant performance improvements on various benchmark datasets, showcasing its adaptability and effectiveness [19][23]. Conclusion - MokA addresses the oversight of modality differences in current fine-tuning paradigms, providing a new direction for multimodal large model fine-tuning [23].

Core Viewpoint - The article introduces the LoRI technology, which demonstrates that significantly reducing the trainable parameters of LoRA can still maintain strong model performance, achieving comparable or superior results to full fine-tuning and other methods while using only 5% of LoRA's parameters [1][9]. Summary by Sections LoRA and Its Limitations - LoRA is widely adopted for parameter-efficient fine-tuning (PEFT) but still incurs significant memory overhead, especially in large models [3][4]. - Recent research indicates substantial redundancy in incremental parameters, prompting the development of LoRI, which reduces the number of trainable parameters while preserving model knowledge [4]. LoRI Methodology - LoRI keeps the low-rank matrix A fixed as a random projection and uses a task-specific sparse mask to train matrix B, allowing for significant parameter reduction [4][13]. - Even with 90% sparsity in B, LoRI maintains good performance, indicating that the adaptation process does not require updating A [4][17]. Multi-Task Learning and Adapter Merging - Multi-task learning is essential for creating versatile models, but training on mixed datasets is costly. LoRI allows for the merging of existing models without retraining, effectively combining LoRA adapters for multi-task capabilities [7]. - Directly merging heterogeneous LoRA can lead to parameter interference, but LoRI mitigates this by mapping task-specific adapters to nearly orthogonal subspaces [7][20]. Continuous Learning and Safety - LoRI provides a lightweight continuous learning method that maintains safety while adapting to new tasks, addressing the challenge of catastrophic forgetting [8][22]. - The two-phase training process for safety adapters shows that LoRI-S outperforms other methods in retaining safety alignment, even under aggressive sparsity [22][23]. Performance Evaluation - Extensive experiments on various benchmarks show that LoRI achieves or exceeds the performance of full fine-tuning and other PEFT methods while using 95% fewer trainable parameters [9][19]. - In single-task performance, LoRI variants demonstrate competitive results across natural language understanding, mathematics, programming, and safety tasks [19][20]. Conclusion - Overall, LoRI presents an effective and lightweight approach to building safe adapters that support downstream task adaptation while maintaining alignment [23].