#parameter-adaptation News & Analysis

2 articles tagged with #parameter-adaptation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · May 286/10

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EvoSpec: Evolving Speculative Decoding via Real-Time Vocabulary and Parameter AdaptationTarget

EvoSpec introduces a dynamic framework for accelerating Large Language Model inference through real-time adaptation of vocabulary and parameters in speculative decoding. By addressing the vocabulary bottleneck that causes performance degradation in specialized domains, EvoSpec achieves 1.13x speedup improvements over static baselines while reducing memory overhead by 27%.

AINeutralarXiv – CS AI · May 116/10

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Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation

Researchers propose MoLF (Mixture of LoRA and Full Fine-Tuning), a hybrid framework that dynamically routes gradient updates between full fine-tuning and low-rank adaptation during LLM training. The approach addresses limitations of relying solely on either method, achieving competitive or superior performance across diverse tasks while maintaining training stability and memory efficiency.