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English(EN) BUSTA: Teaching Small Language Models to Reason About Binary Execution

训练小型语言模型推理二进制执行，达到有竞争力的准确率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了BUSTA，一种训练小型语言模型推理二进制执行的方法，这是逆向工程的关键技能。通过使用逆向基本块执行对Qwen3-1.7B和Nemotron-3-Nano等模型进行微调，它们显著提高了预测输入寄存器值的准确性。虽然前沿模型仍然表现更好，但经过微调的Nemotron-3-Nano以极少的参数实现了有竞争力的结果，证明了针对特定任务进行定向训练的有效性。 AI

影响 增强了小型语言模型在逆向工程等专业任务中的能力，可能减少对更大、资源密集型模型的依赖。

排序理由 该集群描述了一篇研究论文，详细介绍了一种训练语言模型执行特定任务的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

训练小型语言模型推理二进制执行，达到有竞争力的准确率

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该集群描述了一篇研究论文，详细介绍了一种训练语言模型执行特定任务的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ritika Poddar, Aidan Fennelly, Deirdre Murphy, Eric Wolos, Corre L. Steele, Michael Doyle ·

    BUSTA：教小语言模型推理二进制执行

    arXiv:2610.36288v1 Announce Type: cross Abstract: Large Language Models (LLM) are increasingly applied to Reverse Engineering (RE) workflows. RE, however, differs from common LLM coding tasks as it requires models to reason about low-level program behavior. Furthermore, the deplo…

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