RL with Verifiable Rewards
Also written as RLVR, Reinforcement Learning with Verifiable Rewards, RL Environments, Reinforcement Fine-Tuning, Reward Modeling
Training a model with reinforcement learning on tasks where the answer can be checked automatically — did the code pass its tests, is the maths answer correct — instead of relying on human ratings. This is how reasoning and coding models are mostly trained today. Building 'RL environments' (realistic practice tasks with automatic grading) has become its own fast-growing job.
Think of it like
Practising with an answer key: the model attempts thousands of problems and learns from which attempts were marked right.
Junior or senior?
Rare and sought after, mostly at AI labs and companies building their own models.
Senior sounds like
Has designed reward checks and caught a model gaming them (scoring well without actually solving the task).
Ask them
“How did you check the model's answers automatically, and did it ever find a way to score well without really solving the task?”