Categories: FAANG

Distillation Scaling Laws

We propose a distillation scaling law that estimates distilled model performance based on a compute budget and its allocation between the student and teacher. Our findings mitigate the risks associated with large-scale distillation by enabling compute-optimal allocation for both the teacher and student to maximize student performance. We provide compute-optimal distillation recipes for two key scenarios: when a teacher already exists, and when a teacher needs training. In settings involving many students or an existing teacher, distillation outperforms supervised learning up to a compute level…
AI Generated Robotic Content

Recent Posts

VHS filters work great with AI footage (WAN 2.2 + NTSC-RS)

submitted by /u/mtrx3 [link] [comments]

17 hours ago

Algorithm Showdown: Logistic Regression vs. Random Forest vs. XGBoost on Imbalanced Data

Imbalanced datasets are a common challenge in machine learning.

17 hours ago

Unlock global AI inference scalability using new global cross-Region inference on Amazon Bedrock with Anthropic’s Claude Sonnet 4.5

Organizations are increasingly integrating generative AI capabilities into their applications to enhance customer experiences, streamline…

17 hours ago

Connect Spark data pipelines to Gemini and other AI models with Dataproc ML library

Many data science teams rely on Apache Spark running on Dataproc managed clusters for powerful,…

17 hours ago

The Lenovo Go S Is $120 Off

The upgraded version of the Legion Go S with SteamOS makes for a nice Steam…

18 hours ago

AI could make it easier to create bioweapons that bypass current security protocols

Artificial intelligence is transforming biology and medicine by accelerating the discovery of new drugs and…

18 hours ago