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High Entropy & KL Divergence Token Masking for SFT

2026-05-30 Science & Technology
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All rights w/ authors: Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models Qi Liu1, Mingdi Sun1, Yongyi He1, Zhi Zheng1, Tong Xu1, Yi Zheng2, Zhefeng Wang2, Enhong Chen1 from 1 University of Science and Technology of China 2 Huawei Cloud arXiv:2605.29303 EKSFT #airesearch #aiexplained #techexplained #entropy #education

Top Comments (6)

@michaelshane 2026-05-30

Truly love your content while I am fine tuning my agent ux/ui I am incorporating alot of your content and my agent is becoming more natural and easy to deal with. Still having some memory issues and issues with utilization of all systems. Some of it is constantly degraded and unsure how to tighten it all up.... still working. And thank you.

0
@marcfruchtman9473 2026-05-30

Great stuff, I often spend a lot of time trying to learn from the images, but when the AI images of "improvised" formulas that are simply "wrong" with labels that are simply "wrong", it makes it harder. It takes some extra work, but making sure those details are correct is important.

1
@UnmovedMover-i8w 2026-05-30

It seems the benchmarks are not measuring the behavior the model is actually optimizing for. If the objective is to prevent catastrophic forgetting, the evaluation should include tasks that are specifically designed to cause or known to cause catastrophic forgetting; otherwise, the benchmark may fail to capture whether the objective is being achieved.

1
@JoshuaC0rbit 2026-05-30

This would have been nice to have 3 years ago.

1 1 replies
@Ruhgtfo 2026-06-01

Welcome to LoRA

0
@EnergiaEnergy 2026-05-30

❤ definitivamente no deberían de usar eso como cimentación de sus modelos sino como forma de expresión e integración de expresiones para los modelos y ya fuera de eso no parece un parámetro útil

1 2 replies

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