Introduction When writers think about LaTeX, they think about academic papers. Dense research documents filled with equations, citations, and footnotes. They don’t think about fantasy novels, thriller manuscripts, or literary fiction. That’s a missed opportunity. LaTeX—the typesetting system beloved by mathematicians and scientists—offers creative writers something word processors can’t match: programmable consistency. The same features that let physicists define custom notation for quantum equations let novelists define custom styling for character dialogue, narrative voices, and recurring textual elements. […]

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I’ve been trying to RLVR fine tune a LLM with GRPO, the issue is there doesn’t seem to be one go to library that u can use. TRL works and is the most stable with best documentation but it’s limited in terms of async rollouts, environments, etc.. Stuff like skyrl, agent gym rl, agent lightning have steep learning curves and expect you to have really powerful infra. What I’m looking to is build a custom environment, multi turn […]

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arXiv:2601.19005v1 Announce Type: cross Abstract: With the advancement of machine learning and artificial intelligence technologies, recommender systems have been increasingly used across a vast variety of platforms to efficiently and effectively match users with items. As application contexts become more diverse and complex, there is a growing need for more sophisticated recommendation techniques. One example is the composite item (for example, fashion outfit) recommendation where multiple levels of user preference information might be available and relevant. In this […]

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Centralized value learning is often assumed to improve coordination and stability in multi-agent reinforcement learning, yet this assumption is rarely tested under controlled conditions. We directly evaluate it in a fully tabular predator-prey gridworld by comparing independent and centralized Q-learning under explicit embodiment constraints on agent speed and stamina. Across multiple kinematic regimes and asymmetric agent roles, centralized learning fails to provide a consistent advantage and is frequently outperformed by fully independent learning, even under full observability and […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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TheoryFest 2026 will hold workshops during the STOC 2026 conference week, June 22–26, 2026, in Salt Lake City, Utah, USA. We invite groups of interested researchers to submit workshop proposals!  See here for more details: https://acm-stoc.org/stoc2026/callforworkshops.html Submission deadline: March 6, 2026

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LiDAR-based 3D object detection plays an essential role for autonomous driving vehicles under poor lighting condition environments. With LiDAR data, the significance of point cloud technologies is increasing important since Li-DAR sensors are largely cost down. However, the sparsity of point cloud poses a challenge for 3D object detection such that the advancements of sparse convolutional networks could be further investigated. Considering multiscale feature fusion mechanism can improve the object detection performances by using the rich inform among […]

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The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

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In the framework of (fuzzy) Multi-Criteria Decision-Making, we propose a method that1 allows the decision maker to subjectively approach the problem by suitably modifying the decision matrix. We consider a decision problem related to a random quantity X with set of values {x1, x2, . . . , xn}, and a set of properties {C1, C2, . . . , Cm}of X. In this setting, the properties Cj are the criteria of the decision problem, the alternatives represent […]

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