Katia Schwerzmann
@katschwerzmann.bsky.social
250 followers 360 following 25 posts
Philosophy, Technology, and the Body—Toward Justice www.katiaschwerzmann.net
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Reposted by Katia Schwerzmann
kwi-essen.bsky.social
#KWIBlog: In today's blog text, former Thyssen Fellow @katschwerzmann.bsky.social investigates the role of reading in the context of new developments in AI, stressing the need for ongoing investment in close and critical reading that considers AI practices and limitations.
🔎 tinyurl.com/25upvwyj
katschwerzmann.bsky.social
Ich hätte Interesse. Ist das Ticket noch zu haben?
Reposted by Katia Schwerzmann
benpatrickwill.bsky.social
A social sciences and humanities reading list on AI in education 🧵
katschwerzmann.bsky.social
Thank you for building this reading list on AI and eduction!
Reposted by Katia Schwerzmann
bildoperationen.bsky.social
Very timely and necessary critical intervention by @katschwerzmann.bsky.social ‬– highly recommended: «By embracing LLMs before developing any critical framework for their use in pedagogical and research contexts, the University allows itself to be governed by the contingent, ever-evolving ...
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Ruled by the Representation Space: On the University’s Embrace of Large Language Models
arxiv.org
katschwerzmann.bsky.social
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.
Reposted by Katia Schwerzmann
alexcampolo.bsky.social
Totally vague PR blog post as we now expect from OpenAI - remember when they used to publish research? But I am nonetheless interested in the conceptualization of ML "pathologies" - will have to think further about "sycophancy" as political problem... openai.com/index/sycoph...
Sycophancy in GPT-4o: What happened and what we’re doing about it
We have rolled back last week’s GPT‑4o update in ChatGPT so people are now using an earlier version with more balanced behavior. The update we removed was overly flattering or agreeable—often describe...
openai.com
Reposted by Katia Schwerzmann
alexcampolo.bsky.social
Really looking forward to the "New Reading Scenes" workshop later this week, organized by @katschwerzmann.bsky.social at @kwi-essen.bsky.social & @sfb1567.bsky.social. Remote registration is available: docs.google.com/forms/d/e/1F...
New Reading Scenes Workshop Poster New Reading Scenes Workshop Schedule
Reposted by Katia Schwerzmann
rcmeg.bsky.social
Not to put too fine a point on it, but a week ago, the CSU closed 6 departments (among them, philosophy, women's and gender studies, and economics) and fired 46 tenure-line faculty at Sonoma State. One of those faculty studies AI ethics.

They fired the AI ethics professor. Then, this.
katschwerzmann.bsky.social
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.
katschwerzmann.bsky.social
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).
katschwerzmann.bsky.social
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?
katschwerzmann.bsky.social
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).
katschwerzmann.bsky.social
"We create new SFT data through rejection sampling on the RL checkpoint, combined with supervised data from DeepSeek-V3 in domains such as writing, factual QA, and self-cognition, and then retrain the DeepSeek-V3-Base model" (p.2). This seems to be a quite classical fine-tuning process.
katschwerzmann.bsky.social
To tackle this issue, the "DeepSeek-V3-Base model" is fine-tuned using the kind of data commonly used in supervised fine-tuning (SFT):
katschwerzmann.bsky.social
"Pure RL" means here RL that doesn't rely on supervised learning with annotated data. But then, one reads on p. 2 that "DeepSeek-R1-Zero encounters challenges such as poor readability, and language mixing," which is indeed quite a problem for a large language model.
katschwerzmann.bsky.social
Reading DeepSeek's paper: "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning," I find the paper somewhat misleading. DeepSeek's claim is that its model obtains excellent reasoning abilities by applying "pure reinforcement learning" onto the pre-trained model.