This paper teaches a model to turn a question about a table into both a short answer and a clear, correct chart.
ThinkRL-Edit teaches an image editor to think first and draw second, which makes tricky, reasoning-heavy edits much more accurate.
This paper shows that training a language model with reinforcement learning on just one super well-designed example can boost reasoning across many school subjects, not just math.
Talk2Move is a training recipe that lets an image editor move, rotate, and resize the exact object you mention using plain text, while keeping the rest of the picture stable.
Falcon-H1R is a small (7B) AI model that thinks really well without needing giant computers.
Visual Autoregressive (VAR) models draw whole grids of image tokens at once across multiple scales, which makes standard reinforcement learning (RL) unstable.
MDAgent2 is a special helper built from large language models (LLMs) that can both answer questions about molecular dynamics and write runnable LAMMPS simulation code.
Modern AI models can get very good at being correct, but in the process they often lose their ability to think in many different ways.
CPPO is a new way to fine‑tune vision‑language models so they see pictures more accurately before they start to reason.
The paper teaches small language models to predict open-ended future events by turning daily news into thousands of safe, graded practice questions.
FIGR is a new way for AI to ‘think by drawing,’ using code to build clean, editable diagrams while it reasons.
GARDO is a new way to fine-tune text-to-image diffusion models with reinforcement learning without getting tricked by bad reward signals.