#reward-model
4 articles
01
·Training & Alignment·★ MEMBER·PAPER·12 min read
Paper Walkthrough: StudentSim — Training a Simulator That Is Actually *That* Student
How to build the practice students an AI tutor needs, by training on real learner records. From the two-axis definition of behavioral fidelity and guidance responsiveness, through the pooled-then-specialized pipeline, to using the simulator as a reward model.
02
·Paper Deep-Dives·★ MEMBER·PAPER·12 min read
Paper Walkthrough — J-Zero: Growing the Challenger, the Solver, and the Judge Together from Zero Data
A model that writes its own problems, solves them, and grades them — with no external data and no human labels. J-Zero's twist is that the grader learns too, which is what lets it keep improving past the two-iteration wall where prior methods stall.
03
·Inference & Serving·★ MEMBER·PAPER·13 min read
Paper explained: Self-OPD — an image generator that distills itself, with no teacher
No teacher model at all: at every denoising step the student spawns K copies of itself, scores them, and pulls toward the good branches while pushing away from the bad ones. A ground-up walkthrough of Self-OPD, an alignment method for flow matching image generators.
04
·Agents·★ MEMBER·PAPER·10 min read
Paper Explained: OSReward — Can You Trust the AI That Grades AI? Remeasuring Rewards for Computer-Use Agents
The VLM judges that decide whether an agent driving a PC or a phone succeeded turn out to share a leniency bias that reads failure as success — a paper that exposes it with 1,019 human-verified trajectories and closes the gap with OS-Shepherd, an open reward model 30–60× cheaper, explained from zero background.