Hugging Face Trending Papers
Reinforcement Learning-Guided Graph Transformations for SpTRSV Optimization
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arXiv Machine Learning
2d ago
Reinforcement Learning-Guided Graph Transformations for SpTRSV Optimization
arXiv:2609.40159v1 Announce Type: cross Abstract: Sparse triangular solve (SpTRSV) is a fundamental kernel in numerous scientific and engineering applications. However, the data dependencies inherent...
arXiv Machine Learning
Aug 17
Learning-Guided Sparsification of Dynamic Graphs in Robotic Exploration
arXiv:2604. 16509v2 Announce Type: replace-cross Abstract: Many robotic exploration algorithms rely on graph structures for frontier-based exploration and dynamic path planning.
arXiv AI
Jun 4
Outcome-Based RL Provably Leads Transformers to Reason, but Only With the Right Data
arXiv:2601. 15158v4 Announce Type: replace-cross Abstract: Transformers trained via Reinforcement Learning (RL) with outcome-based supervision can spontaneously develop the ability to generate intermediate reasoning steps (Chain-of-Thought).
Hugging Face Trending Papers
Sep 8
Learning to build covering structures with continuous adjustments
The paper presents a reinforcement learning method, HSAC, that builds covering structures without relying on rigid, pre‑planned sequences. It uses graph‑structured state representations and a mixed action space to select blocks and adjust their placement continuously, while an efficient exploration strategy incorporates unilateral edges into graph neural networks. HSAC outperforms the prior hybrid‑PPO approach, shows strong sample efficiency, robustness to hyperparameters, and successfully transfers policies from simulation to a real two‑robot 3D‑printed block construction task.
arXiv AI
Jun 16
StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling
arXiv:2606. 15197v1 Announce Type: cross Abstract: Optimization modeling is inherently hierarchical, requiring a precise sequence of symbolic commitments.
arXiv Machine Learning
Aug 11
Beyond Binary: Continuous State Optimization with Graph-Structured Objectives
arXiv:2608. 09366v1 Announce Type: new Abstract: Large-scale learning systems often face the challenge of balancing multiple, potentially competing objectives, such as fairness, accuracy, and latency.