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ELR:无需一环税的弹性潜在循环语言模型
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2026年10月12日,arXiv 计算与语言栏目发布一手研究,提出 Elastic Latent Recurrence(ELR)。该方法通过门控状态更新、混合循环次数与按循环索引条件化训练,使循环语言模型在仅执行一次循环时即可达到同规模非循环模型的精度,从而摆脱所谓“单循环性能税”;同时每增加一次循环可换取更高准确率。目前公开信息仅涉及该方法的核心机制与上述性能表述,未见后续验证或独立复现结果。
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LatestOct 12
研究者提出ELR,声称单循环即达非循环模型精度,并可随循环次数提升准确率。Timeline
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Oct 12, 2026
- arXiv · Computation and LanguageElastic Latent Recurrence:让循环语言模型摆脱单循环性能税
研究者提出 Elastic Latent Recurrence(ELR),通过门控状态更新、混合循环次数与按循环索引条件化训练,让循环语言模型在单循环时达到同规模非循环模型精度,且每增加一次循环即可换取更高准确率。
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