✨Beyond Binary Preference: Aligning Diffusion Models to Fine-grained Criteria by Decoupling Attributes
📝 Summary:
Current diffusion model alignment struggles with complex, fine-grained human expertise due to simplified preferences. This paper proposes a framework with hierarchical criteria and Complex Preference Optimization CPO, maximizing positive and minimizing negative attributes to improve generation qu...
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04300
• PDF: https://arxiv.org/pdf/2601.04300
==================================
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#DiffusionModels #AIAlignment #MachineLearning #GenerativeAI #PreferenceLearning
📝 Summary:
Current diffusion model alignment struggles with complex, fine-grained human expertise due to simplified preferences. This paper proposes a framework with hierarchical criteria and Complex Preference Optimization CPO, maximizing positive and minimizing negative attributes to improve generation qu...
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04300
• PDF: https://arxiv.org/pdf/2601.04300
==================================
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✓ https://t.me/DataScienceT
#DiffusionModels #AIAlignment #MachineLearning #GenerativeAI #PreferenceLearning
✨Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset
📝 Summary:
This paper introduces IMDD-1M, a large dataset of 1 million industrial defect image-text pairs. It enables training a vision-language foundation model tailored for industrial use. This model achieves comparable performance with less data for specialized tasks, promoting data-efficient quality ins...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24160
• PDF: https://arxiv.org/pdf/2512.24160
==================================
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#IndustrialAI #VisionLanguageModel #DefectDetection #MultimodalAI #ComputerVision
📝 Summary:
This paper introduces IMDD-1M, a large dataset of 1 million industrial defect image-text pairs. It enables training a vision-language foundation model tailored for industrial use. This model achieves comparable performance with less data for specialized tasks, promoting data-efficient quality ins...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24160
• PDF: https://arxiv.org/pdf/2512.24160
==================================
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#IndustrialAI #VisionLanguageModel #DefectDetection #MultimodalAI #ComputerVision
✨AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering
📝 Summary:
AgentDevel reframes LLM agent improvement as release engineering, treating agents as shippable software. It emphasizes stable, auditable improvements through an externalized pipeline that prioritizes non-regression, leading to more reliable and traceable agent development.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04620
• PDF: https://arxiv.org/pdf/2601.04620
==================================
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✓ https://t.me/DataScienceT
#LLMAgents #ReleaseEngineering #SoftwareDevelopment #AIResearch #MLOps
📝 Summary:
AgentDevel reframes LLM agent improvement as release engineering, treating agents as shippable software. It emphasizes stable, auditable improvements through an externalized pipeline that prioritizes non-regression, leading to more reliable and traceable agent development.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04620
• PDF: https://arxiv.org/pdf/2601.04620
==================================
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#LLMAgents #ReleaseEngineering #SoftwareDevelopment #AIResearch #MLOps
✨VERSE: Visual Embedding Reduction and Space Exploration. Clustering-Guided Insights for Training Data Enhancement in Visually-Rich Document Understanding
📝 Summary:
VERSE analyzes Vision-Language Models by visualizing latent representations to find error-prone clusters. It guides synthetic data generation to boost performance in these areas. This significantly improves F1 scores, allowing on-premise models to match or exceed top SaaS solutions.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05125
• PDF: https://arxiv.org/pdf/2601.05125
• Project Page: https://huggingface.co/spaces/de-Rodrigo/Embeddings
• Github: https://github.com/nachoDRT/VrDU-Doctor
==================================
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#VisionLanguageModels #DeepLearning #EmbeddingVisualization #SyntheticData #DocumentUnderstanding
📝 Summary:
VERSE analyzes Vision-Language Models by visualizing latent representations to find error-prone clusters. It guides synthetic data generation to boost performance in these areas. This significantly improves F1 scores, allowing on-premise models to match or exceed top SaaS solutions.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05125
• PDF: https://arxiv.org/pdf/2601.05125
• Project Page: https://huggingface.co/spaces/de-Rodrigo/Embeddings
• Github: https://github.com/nachoDRT/VrDU-Doctor
==================================
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#VisionLanguageModels #DeepLearning #EmbeddingVisualization #SyntheticData #DocumentUnderstanding
✨ProFuse: Efficient Cross-View Context Fusion for Open-Vocabulary 3D Gaussian Splatting
📝 Summary:
ProFuse enhances open-vocabulary 3DGS understanding via an efficient, context-aware framework. It uses a pre-registration phase to fuse semantic features onto Gaussians for cross-view coherence, completing semantic attachment twice as fast as SOTA.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04754
• PDF: https://arxiv.org/pdf/2601.04754
• Project Page: https://chiou1203.github.io/ProFuse/
• Github: https://chiou1203.github.io/ProFuse/
==================================
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#3DGaussianSplatting #ComputerVision #OpenVocabulary #3DReconstruction #DeepLearning
📝 Summary:
ProFuse enhances open-vocabulary 3DGS understanding via an efficient, context-aware framework. It uses a pre-registration phase to fuse semantic features onto Gaussians for cross-view coherence, completing semantic attachment twice as fast as SOTA.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04754
• PDF: https://arxiv.org/pdf/2601.04754
• Project Page: https://chiou1203.github.io/ProFuse/
• Github: https://chiou1203.github.io/ProFuse/
==================================
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#3DGaussianSplatting #ComputerVision #OpenVocabulary #3DReconstruction #DeepLearning
✨Few Tokens Matter: Entropy Guided Attacks on Vision-Language Models
📝 Summary:
Targeting high-entropy tokens in vision-language models causes significant semantic degradation with reduced budgets. This attack strategy reveals critical transferable safety risks across different VLM architectures.
🔹 Publication Date: Published on Dec 26, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21815
• PDF: https://arxiv.org/pdf/2512.21815
==================================
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#VisionLanguageModels #AdversarialAI #AIsecurity #MachineLearning #DeepLearning
📝 Summary:
Targeting high-entropy tokens in vision-language models causes significant semantic degradation with reduced budgets. This attack strategy reveals critical transferable safety risks across different VLM architectures.
🔹 Publication Date: Published on Dec 26, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21815
• PDF: https://arxiv.org/pdf/2512.21815
==================================
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#VisionLanguageModels #AdversarialAI #AIsecurity #MachineLearning #DeepLearning
✨Multi-Agent Software Development through Cross-Team Collaboration
📝 Summary:
Existing multi-agent LLM software development yields a single solution, missing better alternatives. We introduce Cross-Team Collaboration CTC, a framework where multiple agent teams propose and communicate diverse decisions. This significantly improves software quality and generalizes well.
🔹 Publication Date: Published on Jun 13, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2406.08979
• PDF: https://arxiv.org/pdf/2406.08979
• Github: https://github.com/OpenBMB/ChatDev
✨ Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList
==================================
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#MultiAgentSystems #LLMAgents #SoftwareDevelopment #AICollaboration #AIResearch
📝 Summary:
Existing multi-agent LLM software development yields a single solution, missing better alternatives. We introduce Cross-Team Collaboration CTC, a framework where multiple agent teams propose and communicate diverse decisions. This significantly improves software quality and generalizes well.
🔹 Publication Date: Published on Jun 13, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2406.08979
• PDF: https://arxiv.org/pdf/2406.08979
• Github: https://github.com/OpenBMB/ChatDev
✨ Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList
==================================
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#MultiAgentSystems #LLMAgents #SoftwareDevelopment #AICollaboration #AIResearch
✨CoV: Chain-of-View Prompting for Spatial Reasoning
📝 Summary:
Chain-of-View CoV prompting enhances spatial reasoning in 3D embodied question answering for vision-language models. It actively explores environments by selecting question-aligned views and iteratively adjusting camera positions to gather context, leading to significant performance gains across ...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05172
• PDF: https://arxiv.org/pdf/2601.05172
==================================
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✓ https://t.me/DataScienceT
#SpatialReasoning #VisionLanguageModels #EmbodiedAI #Prompting #AI
📝 Summary:
Chain-of-View CoV prompting enhances spatial reasoning in 3D embodied question answering for vision-language models. It actively explores environments by selecting question-aligned views and iteratively adjusting camera positions to gather context, leading to significant performance gains across ...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05172
• PDF: https://arxiv.org/pdf/2601.05172
==================================
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#SpatialReasoning #VisionLanguageModels #EmbodiedAI #Prompting #AI
✨One Sample to Rule Them All: Extreme Data Efficiency in RL Scaling
📝 Summary:
This paper demonstrates extreme data efficiency in RL for LLMs. A single, carefully designed training sample, called polymath learning, significantly enhances multidisciplinary reasoning, outperforming traditional methods that rely on large datasets. The findings suggest sample quality and design...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03111
• PDF: https://arxiv.org/pdf/2601.03111
==================================
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✓ https://t.me/DataScienceT
#ReinforcementLearning #LLMs #DataEfficiency #AI #DeepLearning
📝 Summary:
This paper demonstrates extreme data efficiency in RL for LLMs. A single, carefully designed training sample, called polymath learning, significantly enhances multidisciplinary reasoning, outperforming traditional methods that rely on large datasets. The findings suggest sample quality and design...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03111
• PDF: https://arxiv.org/pdf/2601.03111
==================================
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#ReinforcementLearning #LLMs #DataEfficiency #AI #DeepLearning
❤1
✨LEMAS: Large A 150K-Hour Large-scale Extensible Multilingual Audio Suite with Generative Speech Models
📝 Summary:
LEMAS introduces the largest open-source 150K-hour multilingual speech dataset with word-level timestamps. Models trained on this dataset, LEMAS-TTS and LEMAS-Edit, achieve high-quality zero-shot speech synthesis and seamless speech editing.
🔹 Publication Date: Published on Jan 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04233
• PDF: https://arxiv.org/pdf/2601.04233
• Project Page: https://huggingface.co/spaces/LEMAS-Project/LEMAS-Edit
🔹 Models citing this paper:
• https://huggingface.co/LEMAS-Project/LEMAS-TTS
✨ Datasets citing this paper:
• https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-train
• https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-eval
✨ Spaces citing this paper:
• https://huggingface.co/spaces/LEMAS-Project/LEMAS-TTS
• https://huggingface.co/spaces/LEMAS-Project/LEMAS-Edit
• https://huggingface.co/spaces/Kaiden423/LEMAS-TTS
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
LEMAS introduces the largest open-source 150K-hour multilingual speech dataset with word-level timestamps. Models trained on this dataset, LEMAS-TTS and LEMAS-Edit, achieve high-quality zero-shot speech synthesis and seamless speech editing.
🔹 Publication Date: Published on Jan 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04233
• PDF: https://arxiv.org/pdf/2601.04233
• Project Page: https://huggingface.co/spaces/LEMAS-Project/LEMAS-Edit
🔹 Models citing this paper:
• https://huggingface.co/LEMAS-Project/LEMAS-TTS
✨ Datasets citing this paper:
• https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-train
• https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-eval
✨ Spaces citing this paper:
• https://huggingface.co/spaces/LEMAS-Project/LEMAS-TTS
• https://huggingface.co/spaces/LEMAS-Project/LEMAS-Edit
• https://huggingface.co/spaces/Kaiden423/LEMAS-TTS
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
arXiv.org
LEMAS: Large A 150K-Hour Large-scale Extensible Multilingual Audio...
We present the LEMAS-Dataset, which, to our knowledge, is currently the largest open-source multilingual speech corpus with word-level timestamps. Covering over 150,000 hours across 10 major...
✨Multi-Scale Local Speculative Decoding for Image Generation
📝 Summary:
Multi-Scale Local Speculative Decoding accelerates autoregressive image generation through multi-resolution drafting and spatially informed verification while maintaining semantic quality and perceptu...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05149
• PDF: https://arxiv.org/pdf/2601.05149
• Project Page: https://qualcomm-ai-research.github.io/mulo-sd-webpage/
• Github: https://qualcomm-ai-research.github.io/mulo-sd-webpage
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Multi-Scale Local Speculative Decoding accelerates autoregressive image generation through multi-resolution drafting and spatially informed verification while maintaining semantic quality and perceptu...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05149
• PDF: https://arxiv.org/pdf/2601.05149
• Project Page: https://qualcomm-ai-research.github.io/mulo-sd-webpage/
• Github: https://qualcomm-ai-research.github.io/mulo-sd-webpage
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Scaling Behavior Cloning Improves Causal Reasoning: An Open Model for Real-Time Video Game Playing
📝 Summary:
Behavior cloning demonstrates improved performance and causal reasoning through scaling model size and training data, achieving human-level gameplay in 3D video games. AI-generated summary Behavior cl...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04575
• PDF: https://arxiv.org/pdf/2601.04575
• Project Page: https://elefant-ai.github.io/open-p2p/
• Github: https://github.com/elefant-ai/open-p2p
🔹 Models citing this paper:
• https://huggingface.co/elefantai/open-p2p
✨ Datasets citing this paper:
• https://huggingface.co/datasets/elefantai/p2p-toy-examples
• https://huggingface.co/datasets/elefantai/p2p-full-data
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Behavior cloning demonstrates improved performance and causal reasoning through scaling model size and training data, achieving human-level gameplay in 3D video games. AI-generated summary Behavior cl...
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04575
• PDF: https://arxiv.org/pdf/2601.04575
• Project Page: https://elefant-ai.github.io/open-p2p/
• Github: https://github.com/elefant-ai/open-p2p
🔹 Models citing this paper:
• https://huggingface.co/elefantai/open-p2p
✨ Datasets citing this paper:
• https://huggingface.co/datasets/elefantai/p2p-toy-examples
• https://huggingface.co/datasets/elefantai/p2p-full-data
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Scaling Large-Language-Model-based Multi-Agent Collaboration
📝 Summary:
This paper introduces MacNet for multi-agent collaboration using DAGs for reasoning, outperforming baselines and scaling to many agents. It unveils a collaborative scaling law where emergent abilities appear much earlier than neural emergence.
🔹 Publication Date: Published on Jun 11, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2406.07155
• PDF: https://arxiv.org/pdf/2406.07155
• Project Page: https://github.com/OpenBMB/ChatDev/tree/macnet
• Github: https://github.com/OpenBMB/ChatDev/tree/macnet
✨ Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
This paper introduces MacNet for multi-agent collaboration using DAGs for reasoning, outperforming baselines and scaling to many agents. It unveils a collaborative scaling law where emergent abilities appear much earlier than neural emergence.
🔹 Publication Date: Published on Jun 11, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2406.07155
• PDF: https://arxiv.org/pdf/2406.07155
• Project Page: https://github.com/OpenBMB/ChatDev/tree/macnet
• Github: https://github.com/OpenBMB/ChatDev/tree/macnet
✨ Spaces citing this paper:
• https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨PyramidalWan: On Making Pretrained Video Model Pyramidal for Efficient Inference
📝 Summary:
Pyramidal diffusion models offer efficient inference by varying resolution based on noise. This paper presents a low-cost finetuning pipeline to convert pretrained diffusion models into pyramidal ones, maintaining output quality. They also explore step distillation for enhanced efficiency.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04792
• PDF: https://arxiv.org/pdf/2601.04792
• Project Page: https://qualcomm-ai-research.github.io/PyramidalWan
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Pyramidal diffusion models offer efficient inference by varying resolution based on noise. This paper presents a low-cost finetuning pipeline to convert pretrained diffusion models into pyramidal ones, maintaining output quality. They also explore step distillation for enhanced efficiency.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04792
• PDF: https://arxiv.org/pdf/2601.04792
• Project Page: https://qualcomm-ai-research.github.io/PyramidalWan
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨ReHyAt: Recurrent Hybrid Attention for Video Diffusion Transformers
📝 Summary:
ReHyAt introduces a recurrent hybrid attention mechanism that combines softmax and linear attention benefits, enabling efficient video generation with reduced computational costs and improved scalabil...
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04342
• PDF: https://arxiv.org/pdf/2601.04342
• Project Page: https://qualcomm-ai-research.github.io/rehyat
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
ReHyAt introduces a recurrent hybrid attention mechanism that combines softmax and linear attention benefits, enabling efficient video generation with reduced computational costs and improved scalabil...
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04342
• PDF: https://arxiv.org/pdf/2601.04342
• Project Page: https://qualcomm-ai-research.github.io/rehyat
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Learning User Preferences Through Interaction for Long-Term Collaboration
📝 Summary:
MultiSessionCollab benchmark evaluates agents' ability to learn and adapt to user preferences through persistent memory systems that enhance long-term collaboration quality. AI-generated summary As co...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02702
• PDF: https://arxiv.org/pdf/2601.02702
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
MultiSessionCollab benchmark evaluates agents' ability to learn and adapt to user preferences through persistent memory systems that enhance long-term collaboration quality. AI-generated summary As co...
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02702
• PDF: https://arxiv.org/pdf/2601.02702
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
👩💻 FREE 2026 IT Learning Kits Giveaway
🔥Whether you're preparing for #Cisco #AWS #PMP #Python #Excel #Google #Microsoft #AI or any other in-demand certification – SPOTO has got you covered!
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❤3
✨GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization
📝 Summary:
GRPO in multi-reward RL suffers from reward normalization collapse, hindering training. GDPO resolves this by decoupling individual reward normalization, improving stability and accuracy. GDPO consistently outperforms GRPO across various reasoning tasks.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05242
• PDF: https://arxiv.org/pdf/2601.05242
• Project Page: https://nvlabs.github.io/GDPO/
• Github: https://github.com/NVlabs/GDPO
==================================
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#ReinforcementLearning #MultiRewardRL #PolicyOptimization #MachineLearning #AI
📝 Summary:
GRPO in multi-reward RL suffers from reward normalization collapse, hindering training. GDPO resolves this by decoupling individual reward normalization, improving stability and accuracy. GDPO consistently outperforms GRPO across various reasoning tasks.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05242
• PDF: https://arxiv.org/pdf/2601.05242
• Project Page: https://nvlabs.github.io/GDPO/
• Github: https://github.com/NVlabs/GDPO
==================================
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#ReinforcementLearning #MultiRewardRL #PolicyOptimization #MachineLearning #AI
✨Learnable Multipliers: Freeing the Scale of Language Model Matrix Layers
📝 Summary:
Learnable multipliers address suboptimal weight norms caused by weight decay in large language models. They free the scale of weight matrices using learnable scalar, then per-row and per-column multipliers, outperforming baselines and improving performance with reduced overhead.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04890
• PDF: https://arxiv.org/pdf/2601.04890
• Project Page: https://tiiuae.github.io/Falcon-H1/
• Github: https://github.com/tiiuae/falcon-h1
==================================
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#LLM #DeepLearning #MachineLearning #AI #Optimization
📝 Summary:
Learnable multipliers address suboptimal weight norms caused by weight decay in large language models. They free the scale of weight matrices using learnable scalar, then per-row and per-column multipliers, outperforming baselines and improving performance with reduced overhead.
🔹 Publication Date: Published on Jan 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04890
• PDF: https://arxiv.org/pdf/2601.04890
• Project Page: https://tiiuae.github.io/Falcon-H1/
• Github: https://github.com/tiiuae/falcon-h1
==================================
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#LLM #DeepLearning #MachineLearning #AI #Optimization