SWE-Master is a fully open, step-by-step recipe for turning a regular coding model into a strong software-fixing agent that works across many steps, files, and tests.
Most reinforcement learning agents only get a simple pass/fail reward, which hides how good or bad their attempts really were.
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.
Large language models usually get judged one message at a time, but many real tasks need smart planning across a whole conversation.
Multi-agent systems are like teams of expert helpers; the tricky part is choosing which helpers to ask for each question.
The paper shows that teaching a language model with a special “reward-shaped” next-token objective can make later reinforcement learning (RL) work much better.