not sure how to get this across to non-academics but here goes, Imagine if you were suddenly told 'we decided not to pay your salary', that's kind of what the grant cuts felt like. Now imagine if you were suddenly told 'we are going to set your dog on fire', that's what this feels like:

Want to use AWRS SMC? Check out the GenLM control library: github.com/genlm/genlm-... GenLM supports not only grammars, but arbitrary programmable constraints from type systems to simulators. If you can write a Python function, you can control your language model!

Many LM applications may be formulated as text generation conditional on some (Boolean) constraint. Generate a… - Python program that passes a test suite. - PDDL plan that satisfies a goal. - CoT trajectory that yields a positive reward. The list goes on… How can we efficiently satisfy these? 🧵👇

#ICLR2025 Oral How can we control LMs using diverse signals such as static analyses, test cases, and simulations? In our paper “Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo” (w/ @benlipkin.bsky.social, @alexlew.bsky.social, @xtimv.bsky.social) we:

@xtimv.bsky.social and I were just discussing this interesting comment in the DeepSeek paper introducing GRPO: a different way of setting up the KL loss. It's a little hard to reason about what this does to the objective. 1/

If you're interested in a PhD at the intersection of machine learning and programming languages, consider applying to Yale CS! We're exploring new approaches to building software that draws inferences and makes predictions. See alexlew.net for details & apply at gsas.yale.edu/admissions/ by Dec. 15

Kind of a broken record here but proceedings.neurips.cc/paper_files/... is totally fascinating in that it postulates two underlying, measurable structures that you can use to assess if RL will be easy or hard in an environment

The New Yorker used to have human narrators do pretty great audio versions of selected articles. But then they quietly switched to generic, lifeless AI (with no indication until you click "Listen"). Occasionally they'll still have a human reader, like Sedaris here, and the contrast is insane

Trying something new: A 🧵 on a topic I find many students struggle with: "why do their 📊 look more professional than my 📊?" It's *lots* of tiny decisions that aren't the defaults in many libraries, so let's break down 1 simple graph by @jburnmurdoch.bsky.social 🔗 www.ft.com/content/73a1...

mixtures of circuit approximations of algorithms, I tell you! kernel methods in the space of (short, propositional) programs!! why memorize and interpolate answers when you can memorize and interpolate answer-producing procedures??

Surprisal of title beginning with 'O'? 3.22 Surprisal of 'o' following 'Treatment '? 0.11 Surprisal that title includes surprisal of each title character? Priceless [...I did not know titles could do this]

This is a very cool integration of LLMs + Bayesian methods. LLMs serve as *likelihoods*: how likely would the human be to have issued this (English) command, given a particular (symbolic) plan? No generation, just scoring :) A Bayesian agent can then resolve ambiguity in really sensible ways

It's interesting just how recent this shift was. Autodiff existed but hadn't been adopted by the ML community. Justin Domke had a blog post in 2009 lamenting that so many papers claimed "an efficient algorithm for gradients" as a key technical contribution justindomke.wordpress.com/2009/02/17/a...

Hi Bluesky! My claim to fame is the development of the Alexander Hamiltonian Monte Carlo algorithm. Younger researchers may not realize due to Moore's Law (Lin-Manuel Miranda becomes roughly half as cool every two years), but back when this was published in 2021, it was considered mildly topical