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What would you build if you could run Reinforcement Learning (RL) post-training on a 32B LLM in 4-bit NVFP4—on a...
arXiv:2510.12133v1 Announce Type: new Abstract: With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a...
arXiv:2510.08558v2 Announce Type: replace-cross Abstract: A long-term goal of language agents is to learn and improve through their own experience,...
arXiv:2510.12137v1 Announce Type: new Abstract: Large Language Models (LLMs) hallucinate, generating factually incorrect yet confident assertions. We argue this stems...
arXiv:2510.12121v1 Announce Type: cross Abstract: Precise attribute intensity control–generating Large Language Model (LLM) outputs with specific, user-defined attribute intensities–is crucial...
arXiv:2503.21080v5 Announce Type: replace Abstract: The emergence of autonomous Large Language Model (LLM) agents has created a new ecosystem of...
arXiv:2510.10539v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly applied in sensitive domains such as medicine and...
arXiv:2510.08482v2 Announce Type: replace-cross Abstract: Iconicity, the resemblance between linguistic form and meaning, is pervasive in signed languages, offering a...
arXiv:2510.10560v1 Announce Type: new Abstract: Cross-attention transformers and other multimodal vision-language models excel at grounding and generation; however, their extensive,...


