It is clear that taking samples from a generative model and retraining it on the same samples is useless. It is easy to see that taking samples, selecting the better ones, and retraining on them is essentially a very simple form of RL/REINFORCE for improving a generative model.
In a sense, we are lending our time, minds, compute, and expertise cooking up samples to be consumed by AI models. Pretty soon, I suspect it won't matter "who" wrote which paper, whether a paper was produced entirely by AI, or even whether it contains a typo in a citation.
I've been increasingly thinking that the biggest use of most academic papers will be in training next-gen AI models. Perhaps publication posts can now start with:
"Excited to share that I’ve added a new data point to the web."
This week, we will present our paper “Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards” at #ECCV2026! We've been looking into how to better tune autoregressive image models. 🧵
For the image domain, we develop an FID reward estimator to define the distribution-level reward, which keeps the generated images diverse and helps prevent mode collapse while improving perceptual alignment.
This work is a part of Orhun Buğra Baran’s PhD thesis, and done proudly in collaboration with Prof. Melih Kandemir.
📄 Paper: arxiv.org/abs/2603.23086
💻 Code: github.com/bugrabaran/ar-pol…
📍 ECCV page & Time: eccv.ecva.net/virtual/2026/p…
Gokberk Cinbis retweeted
🤖"Ne seçsem?" diye düşünenleri @METU_ODTU tanıtım fuarına bekliyoruz! ODTÜ'yü seçenler @MetuRomer'de daha lisansta robotik ve yapay zekâ projelerinde çalışma fırsatı bulabiliyorlar! Kısaca: Hangi bölümü seçersen seç, Romer'e geç! 🦾🦿😄
Our new paper on efficient streaming video representations. We define an inference mechanism that progressively adapts to a streaming source and updates the representation over time, without processing each frame from scratch.
sciencedirect.com/science/ar…
arxiv.org/abs/2204.13492
Gokberk Cinbis retweeted
Our grad-level "Deep Learning" course (MIT's 6.7960) is now freely available online through OpenCourseWare: ocw.mit.edu/courses/6-7960-d…
Lecture videos, psets, and readings are all provided.
Had a lot of fun teaching this with @sarameghanbeery and @jxbz!
Gokberk Cinbis retweeted
Darkin is out!
1/n Dark kinases are the understudied kinases with few or no associated phosphosites. For ~95% of reported human phosphosites, we don’t know the cognate kinase. We asked: Can protein language models (pLMs) do zero-shot kinase assignment?
Gokberk Cinbis retweeted
Eruptive activity wiped out all of the habitats at Tica - a famous site among hydrothermal vent scientists
An ERC-funded fluid chemistry team led by Mustafa Yucel are busy understanding the aftermath on deep-ocean chemistry.
#FrontierResearch @METU_ODTU nytimes.com/2025/05/02/scien…
Gokberk Cinbis retweeted
Bu yaz…
Plaj yok, dalga yok, güneş yok.
Yerine: Mars görevi simülasyonu, AI laboratuvarı, ve barbekü var.
Kötü mü sandın? Yanıldın.
🔗 metusummerschool.org/
Happy to share that our paper "Interchangeable Token Embeddings for Extendable Vocabulary and Alpha-Equivalence" has been accepted to #ICML2025 🎉
Interchangeable tokens arise in formal languages if semantics remain invariant under variable renaming (alpha-equivalence).
We aim to build language models that can generalize to new, unseen tokens. To measure model robustness, we introduce the alpha-covariance metric, which demonstrate strong generalization in tasks like solving linear temporal logic formulae with extendable vocabularies.
Check out the preliminary version: arxiv.org/abs/2410.17161 . Joint work with İlker Işık (first author) & Ebru Aydın Göl. We're looking forward to presenting at ICML 2025!
Gokberk Cinbis retweeted
🚀 We proudly lead the @METU_ODTU team in the CHERI mission, selected by CNSA as the flagship international collaboration for the Chang’e-8 lunar program, to design, build, and test AI-driven micro-rovers bound for the Moon’s South Pole in 2028.
Gokberk Cinbis retweeted
RL is not all you need, nor attention nor Bayesianism nor free energy minimisation, nor an age of first person experience. Such statements are propaganda.
You need thousands of people working hard on data pipelines, scaling infrastructure, HPC, apps with feedback to drive benchmarks and data, tons of research and engineering on generative models, data mixtures, ablations, RL/selftraining, etc etc and we will probably need lots of people working hard to figure out safety, causal world models, awareness, models that create abstractions comparable to infinity and zero and use these to predict the existence of things like black holes and suggest experiments to verify such hypothesis, or come up with novel engineering designs to generate energy more efficiently, robotics, etc etc.
It takes thousands of people and many ideas. In the end some simple ideas might become obvious but such obviousness only happens in retrospect. Yes, there is a bitter lesson but if we had followed it, we’d still be doing linear regression with RL. Let’s not oversimplify, but rather honour the research and engineering of thousands of people.
Also, people keep rewriting history. When our language understanding start up (darkbluelabs) was acquired by Google about 10 years ago, we joined DeepMind, where the AGI documents were all about concepts, RL, episodic memories and made it clear that there was no room for language. To be honest, back then such a position wasn’t so crazy. Now it seems silly, but only because of the benefit of hindsight.
There’s no 1 or 10 heroes in the history of AI. There’s many 1000s of hard working students, profs, engineers, operations and support people, product folks, managers, even hedge funds among others. Let’s honour the whole community and not just ceos or the philosophers of Bayes, RL, deep learning, etc. I look forward to learning from the next generation and seeing what they will achieve. To them: Don’t buy the existing narratives blindly, innovate. Remember that just like mathematics, AI will advance one grave at the time.
















