Tag: reinforcement-learning
All the articles with the tag "reinforcement-learning".
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The Credit Assignment Problem: From Rosenblatt's Perceptron to Backpropagation to Quantum Gradients
A long-form, citation-grounded history of the credit assignment problem — the core question of how to apportion blame for a global error across the internal parameters of a learning machine. From Rosenblatt's 1958 perceptron rule, through Linnainmaa, Werbos, and the 1986 Rumelhart-Hinton-Williams paper, to modern alternatives (feedback alignment, equilibrium propagation, predictive coding, forward-forward, synthetic gradients), reinforcement-learning credit assignment, and the frontier of quantum gradients (parameter-shift rule, quantum natural gradient, HHL, and the Abbas et al. NeurIPS 2023 impossibility result).
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The State of Robotics in 2026: A Citation-Grounded Survey of Research and Companies
A citation-grounded survey of robotics research as it stands in May 2026 — VLA foundation models (RT-2, OpenVLA, π0, π0.5, Helix, Gemini Robotics), imitation-learning architectures (Diffusion Policy, ACT/ALOHA, RDT-1B), cross-embodiment data (Open X-Embodiment, DROID), whole-body humanoid control (HOVER, ASAP, OmniH2O), reward design with LLMs (Eureka, DrEureka), world models for robotics (Cosmos, Genie, 1X), simulators (Isaac Lab, Genesis, RoboCasa), and the company landscape (Figure, Tesla, Boston Dynamics, Apptronik, Agility, 1X, Unitree, Physical Intelligence, Skild AI, XPENG, UBTECH, Fourier). With venue-by-venue references from ICLR, ICML, NeurIPS, CoRL, RSS, ICRA, and Science Robotics.
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Notable Papers from ICLR, ICML, NeurIPS, CVPR, EMNLP (2025–2026): An Award-Grounded Reading List
A short, hyperlinked reading list of award-winning and outstanding papers from ICLR 2025/2026, ICML 2025, NeurIPS 2025, CVPR 2025, and EMNLP 2025. One- to three-line summaries with direct arXiv and venue links.
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The Edge of Artificial Intelligence Research: A Citation-Grounded Survey from ICML, ICLR, NeurIPS (2023–2026)
A domain-by-domain survey of the artificial intelligence research frontier, grounded in specific papers from ICML, ICLR, NeurIPS, and adjacent venues (CoRL, CVPR, Nature). Covers natural language processing, speech, video, sound, robotics/VLA, biology, 3D generation, diffusion architecture, and the renaissance of reinforcement learning and world models. The through-line: pretraining is no longer the frontier — test-time compute, generative simulators, and embodied grounding are.