Excited to be at ICML’25!!! I'll present papers on improving LLM inference and evaluation and pseudolabeling-based semi-supervised learning. Come and say hi during these sessions, or chat anytime during the week! [C1]. Prune 'n Predict: Optimizing LLM Decision-making with

Heading to #ICML! I’ll be representing SprocketLab at UW–Madison and Snorkel AI. Reach out if you want to chat about data-centric AI, data development, agents, and foundation models.

Join us today in the morning poster session at #ICML2025. We will talk about some neat ways for reducing uncertainty and improving LLM accuracy at test-time on multi-choice tasks (e.g., tool selection) using conformal prediction and an additional inference round. 📍 East

Next up this morning at #ICML2025, we will be presenting our work on pseudolabeling-based semi-supervised learning (SSL). East Exhibition Hall A&B # E-1304, 11 am to 1:30 pm Paper: openreview.net/pdf?id=w4c5bLk… Pseudolabeling-based SSL relies on the model’s confidence scores and

LLM judges are powerful for automated evaluation but expensive and biased.📣 Meet PAJAMA, a new framework that distills LLM judging logic into a compact, executable form (a new representation), cutting costs from thousands to just cents.🚀 We'll present at ICML PRAL on Friday!

“Rubrics” have become a buzzword in AI, but the concept predates the hype. At Snorkel AI, we’re excited to share a fun primer on what rubric‑based evaluation is—and why it’s critical for today’s generative and agentic models.

first i thought scaling laws originated in OpenAI (2020) then i thought they came from Baidu (2017) now i am enlightened: Scaling Laws were first explored at Bell Labs (1993)

More data ≠ better fine-tuning. Sometimes the 'undertrained' model is more useful because it's still plastic. We need to map the capability vs. adaptability frontier better.

Just read this new paper from Anthropic’s very own Claude called “A Mathematical Theory of Communication” and my brain is broken 🤯

Excited for Conference on Language Modeling next week! I'm taking meetings on scaling laws, hybrid LLMs (Transformer↔SSM/Mamba), agents 🎓I'm also graduating and open to chatting about future opportunities. Grab a slot! docs.google.com/forms/d/e/1FAI… FYI: Tue 4:30–6:30 I’ll be at my poster #COLM2025

Super excited to present our new work on hybrid architecture models—getting the best of Transformers and SSMs like Mamba—at #COLM2025! Come chat with Nicholas Roberts at poster session 2 on Tuesday. Thread below! (1)

Introducing RND1, the most powerful base diffusion language model (DLM) to date. RND1 (Radical Numerics Diffusion) is an experimental DLM with 30B params (3B active) with a sparse MoE architecture. We are making it open source, releasing weights, training details, and code to

Hello world, we wanted to share an early preview of what we're building at Radical Numerics!

🔭 Towards Extending Open dLLMs to 131k Tokens dLLMs behave differently from AutoRegressive models—they lack attention sinks, making long-context extension tricky. A few simple tweaks go a long way!! ✍️blog albertge.notion.site/longdllm 💻code github.com/lbertge/longdl…

We just released the largest open-source diffusion language model (RND1). RND1 is important to me on a personal level: it symbolizes our commitment to open-source exploration of radically different designs for AI at scale — training objectives, architectures, domains. There is

Sliding window attention (SWA) is powering frontier hybrid models for efficiency. Is there something better? Introducing Phalanx, a faster and better quality drop-in replacement for sliding window attention (SWA). Phalanx is a new family of hardware and numerics-aware windowed

The coolest trend for AI is shifting from conversation to action—less talking and more doing. This is also a great opportunity for evals: we need benchmarks that measure utility, including in an economic sense. terminalbench is my favorite effort of this type!

Introducing SnorkelSpatial: A New Benchmark for Evaluating Spatial Reasoning in LLMs Spatial reasoning is everywhere from navigating city maps to understanding molecular interactions. But how well do LLMs handle tasks that require tracking objects moving through space?