MagicBeanStalk (arXiv:2610.12659v1): recursive data synthesis to escape RLVR zero-advantage trap
The paper (arXiv:2610.12659v1) identifies a ‘zero-advantage’ dataset failure mode in RLVR—about 88% of problems in a large open-source math corpus are solved or fail in every rollout for a fixed target policy and budget—and shows filtering alone discards hard, informative problems. It introduces MagicBeanStalk, a top-down decomposition and bottom-up recursive data-synthesis method that grows seed problems into policy- and budget-calibrated families; continual RLVR on these problems raises gpt-oss-120b from 61.2 to 69.8 (+8.6) and DeepSeek v4Flash from 72.4 to 77.9 (+5.4) on 16K-token rollouts, and synthesizes 14K verifiable finance problems from 340 solutions improving SecQue and FinanceBench by 6.9 and 2.4 points respectively.