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RewardMap enhances the capabilities of multimodal large language models in structured vision tasks.
A novel principle-based discriminative constraint optimization framework avoids difficulty bias and training instability.
ReinFlow features a lightweight implementation, built-in exploration capabilities, and broad applicability to various streaming strategy variants.
FHE is widely used in scenarios such as cloud computing security, federated learning, medical data analysis, and financial data collaboration.
BRFL is designed to address the Byzantine attack problem that occurs during model aggregation.
EGMN successfully captured the potential interaction effects between user preferences and video features.
SAC Flow achieves state-of-the-art performance in continuous control and robot operation benchmarks.
UserBench aims to assess and enhance an agent’s ability to understand, interact with, and adapt to real-world user communication.
PLACER is fast and stochastic, and can easily generate prediction sets to map conformational heterogeneity.
With its significant advantages, RAE is poised to become the new default choice for training diffusion Transformers.
Given the limitations of existing fine-tuning techniques such as GRPO, GVPO has emerged as a reliable and versatile post-training paradigm.
ReCA has generalization capabilities in terms of application scenarios and system scale, and the success rate of tasks has been improved by 4.3%.
DexFlyWheel is a scalable and self-improving data generation paradigm for agile operations.
NovaFlow is able to handle rigid, articulated, and deformable objects in different robot forms.
TreeSynth demonstrates exceptional robustness and scalability in large-scale data synthesis.
GTA significantly outperforms standard SFT baselines and state-of-the-art RL methods in multiple text classification benchmarks.
ACE enables agents to improve themselves by dynamically optimizing the input context.
The rise of Vibe coding has not only changed the form of programming, but also reshaped the software development ecosystem.
Analogous to the concept of thought chains in the field of LLM, CoF is applicable to today's generative video models.
Experiments on three alignment capabilities demonstrate the effectiveness of TAE, particularly its realism, which surpasses the baseline 25.8% at a very low cost.
The emergence of the lottery hypothesis has spurred a series of methods for efficiently training neural networks.
TileLang, with its unified block and thread paradigm and transparent scheduling capabilities, meets the powerful functionality and flexibility required for the development of modern AI systems.
RPN and Fast R-CNN are combined into a single network for object detection by sharing convolutional features.
CSA aims to build systems that are not only secure, but also truly helpful.
RewardMap enhances the capabilities of multimodal large language models in structured vision tasks.
A novel principle-based discriminative constraint optimization framework avoids difficulty bias and training instability.
ReinFlow features a lightweight implementation, built-in exploration capabilities, and broad applicability to various streaming strategy variants.
FHE is widely used in scenarios such as cloud computing security, federated learning, medical data analysis, and financial data collaboration.
BRFL is designed to address the Byzantine attack problem that occurs during model aggregation.
EGMN successfully captured the potential interaction effects between user preferences and video features.
SAC Flow achieves state-of-the-art performance in continuous control and robot operation benchmarks.
UserBench aims to assess and enhance an agent’s ability to understand, interact with, and adapt to real-world user communication.
PLACER is fast and stochastic, and can easily generate prediction sets to map conformational heterogeneity.
With its significant advantages, RAE is poised to become the new default choice for training diffusion Transformers.
Given the limitations of existing fine-tuning techniques such as GRPO, GVPO has emerged as a reliable and versatile post-training paradigm.
ReCA has generalization capabilities in terms of application scenarios and system scale, and the success rate of tasks has been improved by 4.3%.
DexFlyWheel is a scalable and self-improving data generation paradigm for agile operations.
NovaFlow is able to handle rigid, articulated, and deformable objects in different robot forms.
TreeSynth demonstrates exceptional robustness and scalability in large-scale data synthesis.
GTA significantly outperforms standard SFT baselines and state-of-the-art RL methods in multiple text classification benchmarks.
ACE enables agents to improve themselves by dynamically optimizing the input context.
The rise of Vibe coding has not only changed the form of programming, but also reshaped the software development ecosystem.
Analogous to the concept of thought chains in the field of LLM, CoF is applicable to today's generative video models.
Experiments on three alignment capabilities demonstrate the effectiveness of TAE, particularly its realism, which surpasses the baseline 25.8% at a very low cost.
The emergence of the lottery hypothesis has spurred a series of methods for efficiently training neural networks.
TileLang, with its unified block and thread paradigm and transparent scheduling capabilities, meets the powerful functionality and flexibility required for the development of modern AI systems.
RPN and Fast R-CNN are combined into a single network for object detection by sharing convolutional features.
CSA aims to build systems that are not only secure, but also truly helpful.