Papers159

The paper teaches large language models to learn from detailed feedback (like error messages) instead of only a simple pass/fail score.

This paper says that to make math-solving AIs smarter, we should train them more on the hardest questions they can almost solve.

OmegaUse is a new AI that can use phones and computers by looking at screenshots and deciding where to click, type, or scroll—much like a careful human user.

DenseGRPO teaches image models using lots of small, timely rewards instead of one final score at the end.

SPARK is a new way to train AI agents that saves compute by exploring more only at the most important moments.

LLMs are usually trained by treating every question the same and giving each one the same number of tries, which wastes compute on easy problems and neglects hard ones.

AdaReasoner teaches AI to pick the right visual tools, use them in the right order, and stop using them when they aren’t helping.

This paper teaches a language-model agent to look up facts in millions of scientific paper summaries and answer clear, single-answer questions.

This paper teaches AI to turn simple dialogue into full movie scenes by first writing a detailed script and then filming it step by step.

SAMTok turns any object’s mask in an image into just two special “words” so language models can handle pixels like they handle text.

The paper introduces Intervention Training (InT), a simple way for a language model to find and fix the first wrong step in its own reasoning using a short, targeted correction.

DARC teaches big language models to get smarter by splitting training into two calm, well-organized steps instead of one chaotic loop.