Samira

Excited to share Soup-of-Experts, a new neural network architecture that, for any given specific task, can instantiate in a flash a small model that is very good on it.

Made with ❤️ at Apple

Thanks to my co-authors David Grangier, Angelos Katharopoulos, and Skyler Seto!

arxiv.org/abs/2502.01804

Distillation Scaling Laws We provide a distillation scaling law that estimates distilled model performance based on a compute budget and its allocation between the student and teacher. Our findings reduce the risks associated ...

Reading "Distilling Knowledge in a Neural Network" left me fascinated and wondering:

"If I want a small, capable model, should I distill from a more powerful model, or train from scratch?"

Our distillation scaling law shows, well, it's complicated... 🧵

arxiv.org/abs/2502.08606

Paper🧵 (cross-posted at X): When does composition of diffusion models "work"? Intuitively, the reason dog+hat works and dog+horse doesn’t has something to do with independence between the concepts being composed. The tricky part is to formalize exactly what this means. 1/

Big thanks to our amazing collaborators: @aggieinca.bsky.social , @harshay-shah.bsky.social , Dan Busbridge, @alaaelnouby.bsky.social , Josh Susskind.

Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models Scaling the capacity of language models has consistently proven to be a reliable approach for improving performance and unlocking new capabilities. Capacity can be primarily defined by two dimensions:...

More experiments and insights are detailed in the paper. Check it out: arxiv.org/abs/2501.12370

However, CoT isn’t parallelizable. Next, It would be exciting to dive into the trade-offs between parallel and sequential compute to understand how we can best combine their strengths!

For presumably reasoning-heavy downstream tasks, sparsity negatively affects transfer. Inference compute plays a crucial role here. Good news: mechanisms like Chain-of-Thought (CoT) can adaptively increase inference compute.

For many downstream tasks, sparsity doesn't affect the relationship between upstream and downstream performance in a few-shot in-context learning.

In practical settings, where total parameters are bounded, the optimal sparsity level depends on model size and training budget, eventually approaching 1.0 as model size grows.

With a fixed training budget, compute-optimal models with higher sparsity not only have more total parameters but also fewer active parameters (i.e., fewer FLOPs per token).

We find that during pretraining, if memory and communication costs are ignored, higher sparsity is always better and Increasing model capacity via total parameters is the optimal strategy.

In MoE models, sparsity can be adjusted by varying total parameters and FLOPs per token (via active parameters). Scaling laws for optimal sparsity levels reveal key insights into the trade-off between parameters vs. compute per token in sparse models at different scales.

🚨 One question that has always intrigued me is the role of different ways to increase a model's capacity: parameters, parallelizable compute, or sequential compute?

We explored this through the lens of MoEs: