Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models (2025 · 47 citations)
Distilling large language models usually means training a student to match the teacher's next-token distribution with a Kullback-Leibler loss.
Revisiting Model Interpolation for Efficient Reasoning (2026 · 6 citations)
The paper looks at efficient reasoning: how to keep the accuracy of long chain-of-thought models while producing fewer tokens.
Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models (2026 · 5 citations)
Compressing small language models (under 8B parameters) to about 2 bits per weight usually ruins their accuracy.
ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection (2026 · 4 citations)
Standard supervised fine-tuning (SFT) trains an LLM to reproduce one reference answer token by token. Valid alternative phrasings are therefore penalized, and the model tends to memorize surface wording. Training on several references per prompt could help, but the authors find it expensive and unstable. They instead…

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Papers of our library cite their works 59 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated 31 October 2026