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News/Shuffle the Context: RoPE-Perturbed Self-Distillation for Long-Context Adaptation
arxiv
PublishedApril 17, 2026 at 4:00 AM
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Shuffle the Context: RoPE-Perturbed Self-Distillation for Long-Context Adaptation

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Publisher summary· verbatim

arXiv:2604.14339v1 Announce Type: new Abstract: Large language models (LLMs) increasingly operate in settings that require reliable long-context understanding, such as retrieval-augmented generation and multi-document reasoning. A common strategy is to fine-tune pretrained short-context models at th

Models mentioned
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  • 01meta-llama logo
    Llama-3-8B
    meta-llama/Llama-3-8B
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#long-context#language-models#self-distillation#robustness

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Mentioned models
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    Llama-3-8B
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Read original ↗All from arxiv →
Tags
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#long-context#language-models#self-distillation#robustness

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arxivRomanized Arabic Across Dialects: Views, Usage Patterns, and Linguistic Variation9harxivDo AI-for-Science Agents Preserve Model Safety?9harxivBehaviour analysis for large language model-driven agents9harxivMPQ: A Message-Passing View of Post-Training Quantization9h
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