There was a great paper at Neurips 2025, where they showed that all "capabilities" that models get via RLVR were in fact already there, if you did pass@k with a sufficiently high k. RLVR makes the models consistently use tricks that tend to work and bring high rewards, which makes them better when k is low, but it suppresses the unusual, which is actually worse when k is high.
There was a great paper at Neurips 2025, where they showed that all "capabilities" that models get via RLVR were in fact already there, if you did pass@k with a sufficiently high k. RLVR makes the models consistently use tricks that tend to work and bring high rewards, which makes them better when k is low, but it suppresses the unusual, which is actually worse when k is high.