✨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

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#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

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#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

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#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

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#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

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#LLM #AI #Reasoning #Optimization #MachineLearning

✨GigaEvo: An Open Source Optimization Framework Powered By LLMs And Evolution Algorithms

📝 Summary:
GigaEvo is an open-source framework for LLM-guided evolutionary computation, providing modular tools for complex optimization. It enhances reproducibility of AlphaEvolve-inspired methods with detailed implementations, validated on challenging problems like Heilbronn triangle placement.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17592
• PDF: https://arxiv.org/pdf/2511.17592
• Project Page: https://airi-institute.github.io/gigaevo-cover/
• Github: https://github.com/FusionBrainLab/gigaevo-core

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#LLM #EvolutionaryAlgorithms #Optimization #OpenSource #AI

✨Rectifying LLM Thought from Lens of Optimization

📝 Summary:
RePro is a novel process-level reward mechanism that refines LLM reasoning by treating chain-of-thought as an optimization process. It uses dual scoring to generate a composite reward, integrated into RL pipelines to enhance performance and reduce suboptimal behaviors.

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01925
• PDF: https://arxiv.org/pdf/2512.01925
• Github: https://github.com/open-compass/RePro

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#LLM #ReinforcementLearning #Optimization #ArtificialIntelligence #DeepLearning

✨Model-Based and Sample-Efficient AI-Assisted Math Discovery in Sphere Packing

📝 Summary:
A model-based AI method using Bayesian optimization and MCTS improves sphere packing upper bounds for dimensions 4-16. It treats SDP construction as a sequential decision process, proving effective for sample-limited math discovery.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04829
• PDF: https://arxiv.org/pdf/2512.04829

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#AI #SpherePacking #MathDiscovery #Optimization #BayesianOptimization

✨TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems

📝 Summary:
TESO is a new metaheuristic framework for simulation optimization that tackles noisy, complex problems. It integrates Tabu List and Elite Memory strategies to dynamically balance exploration and exploitation, demonstrating improved performance.

🔹 Publication Date: Published on Dec 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24007
• PDF: https://arxiv.org/pdf/2512.24007
• Github: https://github.com/bulentsoykan/TESO

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#SimulationOptimization #Metaheuristics #Optimization #BlackBoxOptimization #TabuSearch

✨Nested Learning: The Illusion of Deep Learning Architectures

📝 Summary:
Nested Learning NL models ML as nested optimization problems. It enables expressive algorithms for higher-order learning and continual adaptation, introducing optimizers, self-modifying models, and continuum memory systems.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24695
• PDF: https://arxiv.org/pdf/2512.24695

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#NestedLearning #MachineLearning #DeepLearning #Optimization #AI