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The Karl G. Jansky Very Large Array Local Group L-band Survey (LGLBS)
Eric W. Koch, Adam K. Leroy, Erik W. Rosolowsky, Laura Chomiuk, Julianne J. Dalcanton, Nickolas M. Pingel, Sumit K. Sarbadhicary, Sne\v{z}ana Stanimirovi\'c, Fabian Walter, Haylee N. Archer, Alberto D. Bolatto, Michael P. Busch, Hongxing Chen, Ryan Chown, Harrisen Corbould, Serena A. Cronin, Jeremy Darling, Thomas Do, Jennifer Donovan Meyer, Cosima Eibensteiner, Deidre Hunter, R\'emy Indebetouw, Preshanth Jag…

The Karl G. Jansky Very Large Array Local Group L-band Survey (LGLBS)
We present the Local Group L-Band Survey (LGLBS), a Karl G. Jansky Very Large Array (VLA) survey producing the highest quality 21-cm and 1-2 GHz radio continuum images to date for the six VLA-accessible, star-forming, Local Group galaxies. Leveraging the VLA's spectral multiplexing power, we simultaneously survey the 21-cm line at high 0.4 km/s velocity resolution, the 1-2 GHz polarized continuum, and four OH lines. For the massive spiral M31, the dwarf spiral M33, and the dwarf irregular galax…

Structure of Galois rings and the Gelfand-Kirillov Conjecture
Vyacheslav Futorny, Jonas T. Hartwig, Erich C. Jauch, Jo\~ao Schwarz
https://arxiv.org/abs/2507.10782

Structure of Galois rings and the Gelfand-Kirillov Conjecture
The theory of Galois rings and orders, introduced by Futorny and Ovsienko, has many interesting applications to the structure and representation theory of algebras. This paper focuses on ring theoretical properties of Galois rings. The main technique is based on the fact that our algebras are embedded in a nice way into fixed rings of skew group (or monoid) rings, and via a simple localization procedure many facts about our rings can be deduced from properties of the associated skew group rings…

Bridging Offline and Online Reinforcement Learning for LLMs
Jack Lanchantin, Angelica Chen, Janice Lan, Xian Li, Swarnadeep Saha, Tianlu Wang, Jing Xu, Ping Yu, Weizhe Yuan, Jason E Weston, Sainbayar Sukhbaatar, Ilia Kulikov
https://arxiv.org/abs/2506.21495 https://arxiv.org/pdf/2506.21495 https://arxiv.org/html/2506.21495
arXiv:2506.21495v1 Announce Type: new
Abstract: We investigate the effectiveness of reinforcement learning methods for finetuning large language models when transitioning from offline to semi-online to fully online regimes for both verifiable and non-verifiable tasks. Our experiments cover training on verifiable math as well as non-verifiable instruction following with a set of benchmark evaluations for both. Across these settings, we extensively compare online and semi-online Direct Preference Optimization and Group Reward Policy Optimization objectives, and surprisingly find similar performance and convergence between these variants, which all strongly outperform offline methods. We provide a detailed analysis of the training dynamics and hyperparameter selection strategies to achieve optimal results. Finally, we show that multi-tasking with verifiable and non-verifiable rewards jointly yields improved performance across both task types.
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