The paper reformulates industrial continual learning for LLMs as a closed-loop ecosystem problem, identifies three core challenges, and organizes solutions around five lifecycle design principles.
Heming Xia, Chak Tou Leong, Wenjie Wang, Yongqi Li, and Wenjie Li
2 Pith papers cite this work. Polarity classification is still indexing.
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DeepPrune prunes redundant parallel CoT traces via a judge model for equivalence prediction from partial traces plus online greedy clustering, delivering 65-88% token savings with accuracy within 3 points on AIME and GPQA benchmarks.
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LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning
The paper reformulates industrial continual learning for LLMs as a closed-loop ecosystem problem, identifies three core challenges, and organizes solutions around five lifecycle design principles.
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DeepPrune: Parallel Scaling without Inter-trace Redundancy
DeepPrune prunes redundant parallel CoT traces via a judge model for equivalence prediction from partial traces plus online greedy clustering, delivering 65-88% token savings with accuracy within 3 points on AIME and GPQA benchmarks.