This page shows the most relevant public items for Optimization, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Jul 20, 2017 • arXiv • John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a 'surrogate' objective...
Paper • Feb 15, 2026 • arXiv • DeepMind Research Team
We explore the boundary between simple heuristics and complex neural-cognitive models in combinatorial optimization. This paper demonstrates how hybrid architectures can leverage memory to shape re...
Paper • Apr 3, 2026 • arXiv • Arthur Câmara, Vincent Slot, Jakub Zavrel
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