🔹 Title: Scalable Multi-Task Reinforcement Learning for Generalizable Spatial Intelligence in Visuomotor Agents


• PDF: https://arxiv.org/pdf/2507.23698

• Github: https://github.com/CraftJarvis/ROCKET-3

@Machine_learn

Introduction to Python for
Econometrics, Statistics and Data Analysis

📚 Github

@Machine_learn

با عرض سلام اين مقاله اين هفته سابميت ميشه و فقط يك نفر كم داريم....!
@Raminmousa

Foundations of
Large Language Models

@Machine_learn

Awesome Claude Code 🤝 Awesome Claude Code Agents

📌 Github

@Machine_learn

با عرض سلام ما برای یکی از مقالاتمون در حوزه ی پزشکی نیاز به نفر ۴ ام داریم با قبولی شرایط پرداخت میتونیم اضافه کنیم.

@Raminmousa

📑 Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges


📎 Study the paper


@Machine_learn

با عرض سلام این مورد باقی مونده و این هفته سابمیت مقاله می باشد.
@Raminmousa

The Data Engineering Handbook

📚 Github

@Machine_learn

Crystal Generation with Space Group Informed Transformer

🖥 Github: https://github.com/deepmodeling/crystalformer

📕 Paper: https://arxiv.org/abs/2504.02367v1

🔗 Dataset: https://paperswithcode.com/dataset/alex-20

@Machine_learn

🔹 Title: Scalable Multi-Task Reinforcement Learning for Generalizable Spatial Intelligence in Visuomotor Agents


🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.23698

• PDF: https://arxiv.org/pdf/2507.23698

• Github: https://github.com/CraftJarvis/ROCKET-3

@Machine_learn

با عرض سلام ما برای یکی از مقالاتمون در حوزه ی پزشکی نیاز به نفر ۴ ام داریم با قبولی شرایط پرداخت میتونیم اضافه کنیم.

@Raminmousa

🔹 Title: Visual-CoG: Stage-Aware Reinforcement Learning with Chain of Guidance for Text-to-Image Generation

🔹 Publication Date: Published on Aug 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.18032
• PDF: https://arxiv.org/pdf/2508.18032

@Machine_learn

ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents

📚 Read


@Machine_learn

Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against LLMs

📚 Paper

@Machine_learn

📃 Energy-Based Transformers are Scalable Learners and Thinkers

Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances. In this paper, we ask the question "Is it possible to generalize these System 2 Thinking approaches, and develop models that learn to think solely from unsupervised learning?" Interestingly, we find the answer is yes, by learning to explicitly verify the compatibility between inputs and candidate-predictions, and then re-framing prediction problems as optimization with respect to this verifier. Specifically, we train Energy-Based Transformers (EBTs) -- a new class of Energy-Based Models (EBMs) -- to assign an energy value to every input and candidate-prediction pair, enabling predictions through gradient descent-based energy minimization until convergence. Across both discrete (text) and continuous (visual) modalities, we find EBTs scale faster than the dominant Transformer++ approach during training, achieving an up to 35% higher scaling rate with respect to data, batch size, parameters, FLOPs, and depth. During inference, EBTs improve performance with System 2 Thinking by 29% more than the Transformer++ on language tasks, and EBTs outperform Diffusion Transformers on image denoising while using fewer forward passes.

@Machine_learn