FRAME adds a learnable fractional-Fourier order per expert in a MoE-LoRA setup so that low-rank updates are placed in the domain where they are most compact, yielding gains over fixed-domain baselines on LLaMA-3.1-8B and Qwen2.5-7B.
arXiv preprint arXiv:2502.01427 , year=
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UNVERDICTED 3representative citing papers
Short-term data selectors in multi-stage LLM fine-tuning can slow future learning and increase forgetting, formalized as myopic selection with a proposed LHAS objective to address it.
Sequential post-training of LLMs induces representation collapse that correlates with reduced plasticity, weaker generalization, and poorer calibration, with lightweight interventions tested to mitigate it.
citing papers explorer
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FRAME: Learning the Adaptation Domain with a Mixture of Fractional-Fourier Experts
FRAME adds a learnable fractional-Fourier order per expert in a MoE-LoRA setup so that low-rank updates are placed in the domain where they are most compact, yielding gains over fixed-domain baselines on LLaMA-3.1-8B and Qwen2.5-7B.
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The Long-Term Effects of Data Selection in LLM Fine-Tuning
Short-term data selectors in multi-stage LLM fine-tuning can slow future learning and increase forgetting, formalized as myopic selection with a proposed LHAS objective to address it.
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Representation Collapse in Sequential Post-Training of Large Language Models
Sequential post-training of LLMs induces representation collapse that correlates with reduced plasticity, weaker generalization, and poorer calibration, with lightweight interventions tested to mitigate it.