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arXiv:2604.06542v1 Announce Type: new Abstract: Empirical scaling laws for language models have encouraged the development of ever-larger LLMs, despite their growing computational and memory costs. …
arXiv:2604.06448v1 Announce Type: new Abstract: Prime Video regularly conducts load tests to simulate the viewer traffic spikes seen during live events such as Thursday Night …
arXiv:2604.06465v1 Announce Type: new Abstract: Reasoning models have demonstrated remarkable capabilities in solving complex problems by leveraging long chains of thought. However, this more deliberate …
arXiv:2604.06627v1 Announce Type: new Abstract: In-Context Learning and Chain-of-Thought prompting improve reasoning in large language models (LLMs). These typically come at the cost of longer, …
arXiv:2604.06491v1 Announce Type: new Abstract: We introduce Discrete flow Matching policy Optimization (DoMinO), a unified framework for Reinforcement Learning (RL) fine-tuning Discrete Flow Matching (DFM) …
arXiv:2604.06468v1 Announce Type: new Abstract: Most methods for learning with noisy labels require privileged knowledge such as noise transition matrices, clean subsets or pretrained feature …
arXiv:2604.06543v1 Announce Type: new Abstract: In this work, we demonstrate that reliable stochastic sampling is a fundamental yet unfulfilled requirement for Large Language Models (LLMs) …
arXiv:2604.06613v1 Announce Type: new Abstract: Modern reasoning models continue generating long after the answer is already determined. Across five model configurations, two families, and three …
arXiv:2604.06505v1 Announce Type: new Abstract: Large language models (LLMs) are widely explored for reasoning-intensive research tasks, yet resources for testing whether they can infer scientific …
arXiv:2604.06253v1 Announce Type: new Abstract: Cross-lingual code generation is critical in enterprise environments where multiple programming languages coexist. However, fine-tuning large language models (LLMs) individually …
arXiv:2604.06727v1 Announce Type: new Abstract: Heterogeneity in time series data is more pronounced than in vision or language, as temporal dynamics vary substantially across domains …
arXiv:2604.06507v1 Announce Type: new Abstract: Pashto is absent from Whisper's pre-training corpus despite being one of CommonVoice's largest language collections, leaving off-the-shelf models unusable: all …