✨EVODiff: Entropy-aware Variance Optimized Diffusion Inference

📝 Summary:
EVODiff optimizes diffusion model inference using an entropy-aware variance method. It leverages information theory to reduce uncertainty and minimize errors. This approach significantly outperforms gradient-based solvers, enhancing efficiency and reconstruction quality.

🔹 Publication Date: Published on Sep 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.26096
• PDF: https://arxiv.org/pdf/2509.26096
• Project Page: https://neurips.cc/virtual/2025/poster/115792
• Github: https://github.com/ShiguiLi/EVODiff

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#DiffusionModels #DeepLearning #MachineLearning #Optimization #InformationTheory

✨Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

📝 Summary:
ROLL is an efficient, scalable, and user-friendly library for large-scale reinforcement learning optimization. It features a simplified architecture, parallel training, flexible sample management, and resource mapping for developers and researchers.

🔹 Publication Date: Published on Jun 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.06122
• PDF: https://arxiv.org/pdf/2506.06122
• Github: https://github.com/alibaba/roll

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#ReinforcementLearning #MachineLearning #LargeScaleAI #Optimization #AIResearch

✨The Path Not Taken: RLVR Provably Learns Off the Principals

📝 Summary:
RLVR learns by modifying parameters off principal directions in low-curvature subspaces, appearing sparse due to optimization bias. This distinct optimization regime contrasts with SFT, meaning SFT-era fine-tuning methods are flawed for RLVR.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.08567
• PDF: https://arxiv.org/pdf/2511.08567

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#RLVR #MachineLearning #Optimization #DeepLearning #AIResearch

✨Superpositional Gradient Descent: Harnessing Quantum Principles for Model Training

📝 Summary:
Superpositional Gradient Descent SGD is a new quantum-inspired optimizer. It uses quantum superposition to enhance gradient updates, leading to faster convergence and lower final loss in LLM training than AdamW.

🔹 Publication Date: Published on Nov 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.01918
• PDF: https://arxiv.org/pdf/2511.01918
• Github: https://github.com/The-Aqua-Labs/Superpositional-Gradient-Descent

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#MachineLearning #AI #LLM #QuantumInspired #Optimization

✨Experience-Guided Adaptation of Inference-Time Reasoning Strategies

📝 Summary:
Experience-Guided Reasoner EGuR dynamically generates and optimizes complete computational strategies at inference time using accumulated experience. It adapts LLM calls tools and control logic improving accuracy up to 14 percent and reducing costs by up to 111x.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11519
• PDF: https://arxiv.org/pdf/2511.11519

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #AI #Reasoning #Optimization #MachineLearning