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✨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

✨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

✨RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes

📝 Summary:
RL-AWB is a novel framework for nighttime auto white balance. It combines statistical methods with deep reinforcement learning, mimicking expert tuning to improve color constancy in low-light scenes. The method shows superior generalization across various lighting conditions and includes a new mu...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05249
• PDF: https://arxiv.org/pdf/2601.05249
• Project Page: https://ntuneillee.github.io/research/rl-awb/
• Github: https://github.com/BrianChen1120/RL-AWB

==================================

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#ReinforcementLearning #DeepLearning #ComputerVision #ImageProcessing #AWB

✨Token-Level LLM Collaboration via FusionRoute

📝 Summary:
FusionRoute is a token-level multi-LLM collaboration framework that uses a lightweight router to select optimal experts and add complementary logits, outperforming existing methods in diverse tasks wh...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05106
• PDF: https://arxiv.org/pdf/2601.05106

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨RelayLLM: Efficient Reasoning via Collaborative Decoding

📝 Summary:
RelayLLM enables efficient collaborative reasoning between small and large language models through token-level dynamic invocation, achieving high accuracy with minimal computational overhead. AI-gener...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05167
• PDF: https://arxiv.org/pdf/2601.05167
• Github: https://github.com/Chengsong-Huang/RelayLLM

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twice

📝 Summary:
VideoAuto-R1 framework employs a reason-when-necessary strategy for video understanding, using a Thinking Once, Answering Twice training paradigm with verifiable rewards and confidence-based reasoning...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05175
• PDF: https://arxiv.org/pdf/2601.05175
• Project Page: https://ivul-kaust.github.io/projects/videoauto-r1/
• Github: https://github.com/IVUL-KAUST/VideoAuto-R1/

✨ Spaces citing this paper:
• https://huggingface.co/spaces/sming256/VideoAuto-R1_Demo

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨RoboVIP: Multi-View Video Generation with Visual Identity Prompting Augments Robot Manipulation

📝 Summary:
Collecting diverse robot manipulation data is challenging. This paper introduces visual identity prompting, using exemplar images to guide diffusion models for generating multi-view, temporally coherent data. This augmented data improves robot policy performance in both simulation and real-world ...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05241
• PDF: https://arxiv.org/pdf/2601.05241
• Project Page: https://robovip.github.io/RoboVIP/
• Github: https://robovip.github.io/RoboVIP/

==================================

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#Robotics #AI #GenerativeAI #ComputerVision #MachineLearning

✨AT^2PO: Agentic Turn-based Policy Optimization via Tree Search

📝 Summary:
AT^2PO is a framework for multi-turn agentic reinforcement learning. It uses a turn-level tree search with entropy-guided expansion and turn-wise credit assignment. This improves exploration, reward propagation, and policy optimization, achieving state-of-the-art results.

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04767
• PDF: https://arxiv.org/pdf/2601.04767
• Github: https://github.com/zzfoutofspace/ATPO

==================================

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#ReinforcementLearning #AgenticAI #TreeSearch #PolicyOptimization #ArtificialIntelligence

✨Few Tokens Matter: Entropy Guided Attacks on Vision-Language Models

📝 Summary:
Attacking a few high-entropy tokens in VLMs significantly degrades outputs with reduced budgets. These selective attacks efficiently create harmful outputs and transfer across architectures, exposing new VLM safety weaknesses.

🔹 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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#VLMs #AISafety #AdversarialAI #MachineLearning #AIResearch

✨VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control

📝 Summary:
VerseCrafter is a 4D video world model enabling unified control over camera and object dynamics. It uses a novel 4D Geometric Control representation with 3D Gaussian trajectories for high-fidelity video generation. An automatic data engine addresses training data scarcity.

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05138
• PDF: https://arxiv.org/pdf/2601.05138
• Github: https://sixiaozheng.github.io/VerseCrafter_page/

🔹 Models citing this paper:
• https://huggingface.co/TencentARC/VerseCrafter

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Agent-as-a-Judge

📝 Summary:
Large language models face limitations in evaluating complex, multi-step tasks, prompting the development of agent-based evaluation systems that utilize planning, tool-augmented verification, and mult...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05111
• PDF: https://arxiv.org/pdf/2601.05111
• Github: https://github.com/ModalityDance/Awesome-Agent-as-a-Judge

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨The Illusion of Specialization: Unveiling the Domain-Invariant "Standing Committee" in Mixture-of-Experts Models

📝 Summary:
Mixture of Experts models exhibit a Standing Committee of experts that consistently dominates routing across domains, challenging the assumption of widespread specialization. This reveals a strong structural bias toward centralized computation, limiting effective specialization.

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03425
• PDF: https://arxiv.org/pdf/2601.03425

==================================

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#MixtureOfExperts #DeepLearning #MachineLearning #AISpecialization #NeuralNetworks

✨Plenoptic Video Generation

📝 Summary:
PlenopticDreamer addresses multi-view video re-rendering inconsistency by synchronizing generative hallucinations. It uses an autoregressive model with camera-guided retrieval to ensure spatio-temporal coherence, achieving state-of-the-art results with high fidelity.

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05239
• PDF: https://arxiv.org/pdf/2601.05239
• Project Page: https://research.nvidia.com/labs/dir/plenopticdreamer/

==================================

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#PlenopticVideo #GenerativeAI #VideoGeneration #ComputerVision #DeepLearning

✨CoV: Chain-of-View Prompting for Spatial Reasoning

📝 Summary:
Chain-of-View CoV prompting helps vision-language models improve spatial reasoning in 3D embodied question answering. It actively selects question-aligned views and iteratively adjusts camera positions to gather context, significantly boosting performance without additional training.

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05172
• PDF: https://arxiv.org/pdf/2601.05172
• Github: https://github.com/ziplab/CoV

==================================

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#SpatialReasoning #VisionLanguageModels #PromptEngineering #EmbodiedAI #AIResearch

✨DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs

📝 Summary:
DiffCoT reformulates chain-of-thought reasoning as an iterative denoising process using diffusion principles, enabling unified generation and correction of intermediate steps while maintaining causal ...

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03559
• PDF: https://arxiv.org/pdf/2601.03559

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing

📝 Summary:
Re-Align addresses the gap between understanding and generation in in-context image generation and editing through structured reasoning-guided alignment and reinforcement learning training. AI-generat...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05124
• PDF: https://arxiv.org/pdf/2601.05124
• Project Page: https://hrz2000.github.io/realign/
• Github: https://github.com/hrz2000/realign

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨One Sample to Rule Them All: Extreme Data Efficiency in RL Scaling

📝 Summary:
This paper introduces polymath learning, demonstrating that a single, carefully designed training sample can significantly boost language model reasoning across multiple scientific disciplines. This sample engineering approach outperforms training with larger datasets, emphasizing quality over qu...

🔹 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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#AI #MachineLearning #LLM #DataEfficiency #SampleEngineering

✨DocDancer: Towards Agentic Document-Grounded Information Seeking

📝 Summary:
DocDancer is an end-to-end trained open-source document question answering agent that formulates the task as an information-seeking problem and uses a tool-driven framework with exploration and synthe...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05163
• PDF: https://arxiv.org/pdf/2601.05163

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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