Data mixing enhanced by RL improves evaluation metrics

An RL‑driven data scheduler can lift MMLU performance by 27.5 % relative while achieving a 2.23× higher HumanEval pass@1, and it does so with virtually no extra compute [1]. The scheduler learns a policy that decides, at each step, how many examples from each source task to present to the model. Bec