🤔 Filter by solve rate, reweight by advantage, adaptively tune domain mixtures — the RLVR literature is full of data policies promising better training. But most were introduced inside large training stacks where the base model, verifier, and optimizer settings all changed at once, making it hard to tell whether the data policy itself was doing the work. So the authors froze the GRPO recipe completely and built DataFlex-RL, an evaluation platform that isolates three intervention families — selection, reweighting, and mixture adaptation — and ran 156 training runs across 13 configurations and 12 seeds on Qwen2.5-7B. Uniform sampling alone lifts Overall accuracy from 42.01 to 49.77, a 7.76-point gain from GRPO training. Yet none of the eight selection methods, eight reweighting methods, or three mixture methods beat uniform sampling by a statistically meaningful margin — every confidence interval crossed zero. Title: DataFlex-RL: An Evaluation Platform for RLVR Data Policies URL: An honest negative result that cares more about "does it actually work" than pitching a flashy new method. #ReinforcementLearning# #MachineLearning#