Ximing Lu

Neat technique to creates lots of cheap, auto-graded “reasoning practice” from normal text, so reinforcement learning does not run out of useful problems so fast. "Golden Goose", a simple trick to synthesize unlimited RLVR tasks from unverifiable internet text. " is a way to train AI using books or articles that are full of explanations, even when you cannot easily check if every explanation step is “true” with a normal checker. Most “training with rewards” setups need a clean yes or no score, like “the math answer matches” or “the code passes tests,” so a lot of good learning text gets ignored. Golden Goose turns a normal explanation into a simple quiz by hiding a key middle part of the reasoning and asking the model to pick the missing chunk from 4 or 5 choices. The correct choice is literally the chunk that was removed, so scoring is easy and automatic, which makes it “verifiable” without needing a math proof checker or a code runner. They used this trick to build GooseReason-0.7M, which is 0.7M training questions across math, coding, and science from sources that were previously hard to use for this kind of training. They show that strong models can “saturate” on a fixed RLVR dataset, meaning more training stops helping and can even hurt, and adding fresh GooseReason data makes progress continue again. On Qwen-4B-Instruct, the added data flips a prior -0.79% drop into a +2.27% gain across 15 benchmarks, and in cybersecurity 180K extra tasks give a +4.44% gain across 3 security benchmarks after 100 RL steps. Verification becomes cheap, while data quality depends on the generator LLM and filtering.