I’ve just started learning Reinforcement Learning and finished watching Lecture 4 (Model-Free Prediction) of David Silver’s course. I’m loving the theory and most concepts are clicking (MDPs, Bellman equations), though I sometimes have to pause to check Sutton & Barto when the math gets dense. However, I realized today that I haven’t actually written a single line of code yet. I’m comfortable with general ML and math, but completely new to RL practice. Two questions for those who […]

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arXiv:2603.22964v1 Announce Type: cross Abstract: Generalization is a central concept in machine learning theory, yet for quantum models, it is predominantly analyzed through uniform bounds that depend on a model’s overall capacity rather than the specific function learned. These capacity-based uniform bounds are often too loose and entirely insensitive to the actual training and learning process. Previous theoretical guarantees have failed to provide non-uniform, data-dependent bounds that reflect the specific properties of the learned solution rather than the […]

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Last week, we went over Peter Parker’s best suits in Marvel’s Spider-Man 2. But the game featured another protagonist: Miles Morales. And so today, we’ll go over his best suits. Just like Peter, Miles had some stinkers, but also some gems. Here are his 5 best suits in Marvel’s Spider-Man 2. P.S. I got all these screenshots from this YouTube video. Make sure to check it out! Miles Morales 5 Best Suits in Marvel’s Spider-Man 2 Smoke and Mirrors Suit […]

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arXiv:2603.06652v1 Announce Type: new Abstract: Reinforcement learning has recently improved the reasoning ability of Large Language Models and Multimodal LLMs, yet prevailing reward designs emphasise final-answer correctness and consequently tolerate process hallucinations–cases where models reach the right answer while misperceiving visual evidence. We address this process-level misalignment with PaLMR, a framework that aligns not only outcomes but also the reasoning process itself. PaLMR comprises two complementary components: a perception-aligned data layer that constructs process-aware reasoning data with structured […]

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In API-first systems, poor data quality (invalid emails, duplicate records, etc.) creates unpredictable bugs, forces defensive coding, and makes releases feel risky. This “hidden tax” consumes time and mental energy that should go to building features. The fix? Treat data quality as core infrastructure. By using real-time validation APIs at the point of ingestion, you create predictable systems, simplify business logic, and build developer confidence. This turns a vicious cycle of complexity into a virtuous cycle of velocity […]

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Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale models that are both robust and accurate. To tackle this issue and cater to the learning requirements, we envision using a federated learning framework with a quantum-enhanced model. Specifically, we design a hybrid quantum-classical long-shot-term-memory model (QLSTM) for local training at distributed nodes. It combines the representative power of quantum models in understanding complex relationships within the feature space, and […]

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OpenAI has launched a Codex Chrome extension for Mac and PC to streamline browser-based workflows that were previously difficult to handle via APIs or plugins. This release follows a trend where most users preferred working in a browser after the launch of “Computer Use,” allowing Codex to operate more effectively across various web-based tasks. What the Extension Actually Does Before this release, Codex had access to an in-app browser — a sandboxed browser built into the Codex desktop […]

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arXiv:2501.02624v2 Announce Type: replace-cross Abstract: Approximate Leave-One-Out Cross-Validation (ALO-CV) is a method that has been proposed to estimate the generalization error of a regularized estimator in the high-dimensional regime where dimension and sample size are of the same order, the so-called “proportional regime”. A new analysis is developed to derive the consistency of ALO-CV for non-differentiable regularizers under Gaussian covariates and strong convexity. Using a conditioning argument, the difference between the ALO-CV weights and their counterparts in mean-field […]

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