I’m trying to sanity-check a potential career trajectory and would appreciate some honest feedback. I have a BSc in Computer Science and an MSc in Data Science. I’ve been working as a data scientist in the UK public sector for about four years and currently earn just under £50k. A year ago, I posted on this subreddit about my interest applying RL to Psychology. Well, I’ve recently been accepted into a fully funded Psychology PhD where my research […]

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Reinforcement Learning from Human Feedback (RLHF) has become the standard for aligning Large Language Models (LLMs), yet its efficacy is bottlenecked by the high cost of acquiring preference data, especially in low-resource and expert domains. To address this, we introduce ACTIVEULTRAFEEDBACK, a modular active learning pipeline that leverages uncertainty estimates to dynamically identify the most informative responses for annotation. Our pipeline facilitates the systematic evaluation of standard response selection methods alongside DOUBLE REVERSE THOMPSON SAMPLING (DRTS) and DELTAUCB, […]

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arXiv:2602.12107v1 Announce Type: cross Abstract: We study offline reinforcement learning under $Q^star$-approximation and partial coverage, a setting that motivates practical algorithms such as Conservative $Q$-Learning (CQL; Kumar et al., 2020) but has received limited theoretical attention. Our work is inspired by the following open question: “Are $Q^star$-realizability and Bellman completeness sufficient for sample-efficient offline RL under partial coverage?” We answer in the negative by establishing an information-theoretic lower bound. Going substantially beyond this, we introduce a general framework […]

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arXiv:2602.18450v1 Announce Type: new Abstract: Multi-agent language systems can exhibit a failure mode where a shared dominant context progressively absorbs individual semantics, yielding near-uniform behavior across agents. We study this effect under the name Asymptotic Semantic Collapse in Hierarchical Optimization. In a closed linguistic setting with a Dominant Anchor Node whose semantic state has effectively infinite inertia, we show that repeated interactions with Peripheral Agent Nodes drive an asymptotic alignment that minimizes a global loss. We model semantic […]

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Welcome to HackerNoon’s Meet the Writer Interview series, where we learn a bit more about the contributors that have written some of our favorite stories. Let’s start! Tell us a bit about yourself (name, profession, and personal interests). My name is Norm Bond. I help founders, operators and creators think clearly in an environment that keeps accelerating. My work tracks the intersection of tech, markets and meaning**.** I focus on what happens when systems become powerful faster than […]

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We entered the Release Candidate stage in the Godot 4.3 development cycle a week ago with 4.3 RC 1, which means that all features are in place, the most critical regressions have been tackled, and we’re confident that it’s now ready for general use in the vast majority of cases. A lot of users have been testing the RC 1 snapshot, reporting issues, many of which could be fixed. In the meantime, we’ve kept working on a few […]

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arXiv:2603.02411v1 Announce Type: new Abstract: Dataset Distillation (DD) compresses large datasets into compact synthetic ones that maintain training performance. However, current methods mainly target sample reduction, with limited consideration of data precision and its impact on efficiency. We propose Quantization-aware Dataset Distillation (QuADD), a unified framework that jointly optimizes dataset compactness and precision under fixed bit budgets. QuADD integrates a differentiable quantization module within the distillation loop, enabling end-to-end co-optimization of synthetic samples and quantization parameters. Guided by […]

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