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Want faster #LLM inference without sacrificing accuracy? 🚀 Introducing SD², a novel method using Self-Distilled Sparse Drafters to accelerate speculative decoding. We show that self-data distillation combined #sparsity can be used to train low-latency, well-aligned drafters.🧵

Cited by 100 :)

Excited for #ICML2025 in Vancouver! On Thursday morning, I'm presenting our paper (arxiv.org/pdf/2506.00962) on a critical issue in reinforcement learning: how to correctly handle random time horizons. We've identified incorrect formulas and offer a solution. Let's chat, write me!

We will be presenting Self-Distilled Sparse Drafters at #ICML this afternoon at the Efficient Systems for Foundation Models workshop. Come chat with us about acclerating speculative decoding! 🚀 Cerebras Calgary ML Lab

Personal news: I have defended my PhD thesis “Explaining Representations in Deep Neural Networks” at TU Berlin with summa cum laude (with distinction)! From August, I’ll start a Postdoc at TU München in Explainable Machine Learning group focusing on Mechanistic Interpretability ✨

*sniff* *pulls shirt* You know, this is perfect - *gestures wildly* - this is the ultimate perversity of capitalism at its purest. Here we have Anthropic, this company claiming to build "AI for humanity," and what do they do? They create this digital cocaine, this Claude Code,