Specifications for decentralized learning on resource-constrained edge devices require algorithms that are communication-efficient, robust to data corruption, and lightweight in memory usage. While state-of-the-art gossip-based methods satisfy the first requirement, achieving robustness remains challenging. Asynchronous decentralized ADMM-based methods have been explored for estimating the median, a statistical centrality measure that is notoriously more robust than the mean. However, existing approaches require memory that scales with node degree, making them impractical when memory is limited. In this paper, we […]

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Post-training algorithms based on deep reinforcement learning can push the limits of robotic models for specific objectives, such as generalizability, accuracy, and robustness. However, Intervention-requiring Failures (IR Failures) (e.g., a robot spilling water or breaking fragile glass) during real-world exploration happen inevitably, hindering the practical deployment of such a paradigm. To tackle this, we introduce Failure-Aware Offline-to-Online Reinforcement Learning (FARL), a new paradigm minimizing failures during real-world reinforcement learning. We create FailureBench, a benchmark that incorporates common failure […]

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Fixed-horizon A/B tests are slow and prone to false positives when you peek at data. Sequential testing with mSPRT enables anytime-valid inference, letting teams monitor experiments continuously and stop early without inflating Type I errors. Techniques like linearization and guardrails handle ratio metrics and outliers, ensuring fast, reliable, and statistically rigorous B2C decision-making.

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arXiv:2604.18801v1 Announce Type: new Abstract: Lossy compression is widely used to reduce storage and I/O costs for large-scale particle datasets in scientific applications such as cosmology, molecular dynamics, and fluid dynamics, where clustering structures (e.g., single-linkage or Friends-of-Friends) are critical for downstream analysis; however, existing compressors typically provide only pointwise error bounds on particle positions and offer no guarantees on preserving clustering outcomes, and even small perturbations can alter cluster connectivity and compromise scientific validity. We propose a […]

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arXiv:2601.18913v1 Announce Type: new Abstract: Balancing safety, efficiency, and interaction is fundamental to designing autonomous driving agents and to understanding autonomous vehicle (AV) behavior in real-world operation. This study introduces an empirical learning framework that derives these trade-offs directly from naturalistic trajectory data. A unified objective space represents each AV timestep through composite scores of safety, efficiency, and interaction. Pareto dominance is applied to identify non-dominated states, forming an empirical frontier that defines the attainable region of balanced […]

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I have an intermediate knowledge about ML algorithms and working of LLMs. I have also made projects using regression and classification and Fine tuned LLMs. So my doubt is that can I start learning and RL just by picking up a self car driving project and learn RL while build it. Nerds please tell me or give me a guide and not for a begnner level submitted by /u/Old-Raspberry-3266 [link] [comments]

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We propose Hamiltonian-SMT, the first MARL framework to replace “guess-and-check” evolution with verified Policy Impulses. By modeling the population as a discrete Hamiltonian system, we enforce physical and logical conservation laws: System Energy (E): Formally represents Social Welfare (Global Reward). Momentum (P): Formally represents Behavioral Diversity. Impulse (∆W): A weight update verified by Lean 4 to be Lipschitz-continuous and energy-preserving. submitted by /u/Regular_Run3923 [link] [comments]

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arXiv:2601.02454v1 Announce Type: new Abstract: Software testing has progressed toward intelligent automation, yet current AI-based test generators still suffer from static, single-shot outputs that frequently produce invalid, redundant, or non-executable tests due to the lack of execution aware feedback. This paper introduces an agentic multi-model testing framework a closed-loop, self-correcting system in which a Test Generation Agent, an Execution and Analysis Agent, and a Review and Optimization Agent collaboratively generate, execute, analyze, and refine tests until convergence. By […]

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arXiv:2601.19909v1 Announce Type: new Abstract: Prime generation is a fundamental task in cryptography, number theory, and randomized algorithms. While the classical Sieve of Eratosthenes is simple and efficient in theory, its practical performance on modern central processing units is often limited by memory access inefficiencies. This paper introduces a cache-aware hybrid sieve that integrates segmentation, bit-packing, and cache-line-aligned block processing to optimize memory bandwidth and level one and level two cache locality. The proposed approach reduces memory usage […]

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