Thank you @olivia.science for your reading!

And this is the English version of the open letter against the uncritical adoption of AI technologies in academia: openletter.earth/open-letter-...

Love your music rec!

Ich hätte Interesse. Ist das Ticket noch zu haben?

Thank you for building this reading list on AI and eduction!

Thank you for sharing your reading @bildoperationen.bsky.social. I am glad it resonated. Working at a critique of generative AI in the context of research and education is currently a somewhat lonely endeavor. I hope more researchers will join. We need to tackle this from a plurality of approaches.

Exciting Alex!!!!

The workshop is organized by our Thyssen@KWI Fellow
@katschwerzmann.bsky.social
with talks by @floriansprenger.bsky.social | @alexcampolo.bsky.social |
@rainermuehlhoff.bsky.social | @moritzhiller.bsky.social
Further Information here: www.kulturwissenschaften.de/veranstaltun...

This language signals ML community's tendency—or rather desire—to make the human factor, in particular researchers' judgement, evaluation, and labor, disappear from view and from model training. The rule-based component of the reward model is interesting, though.

One also notices once again the type of naturalizing language that @alexcampolo.bsky.social and I critically analyze in our work. For instance: "During training, DeepSeek-R1-Zero **naturally** emerged with numerous powerful and interesting reasoning behaviors" (p. 3).

So what exactly is new? That LLMs can do well in math reasoning and coding without relying on supervised learning but still don't do well enough in natural language tasks to not rely on supervised fine-tuning in the end?

The pure RL phase concerns math and coding problems only. The reward model assesses the base model's solution to "deterministic" math problems through "rule-based verification of correctness," while for coding problem "a compiler can be used to generate feedback based on predefined test cases"(p.6).