The cheapest model was the most robust to GRPO post-training

RL post-training has become a routine upgrade in the reasoning-model boom, applied as if the gains were a property of the algorithm. A single practitioner's three-run experiment suggests they are a property of the model.

u/john463212 on Reddit's r/MachineLearning built and post-trained three from-scratch LLMs (353M, 316M, 672M) with the same GRPO recipe. The middle-size V2 (316M) saw a +52% jump in WikiText perplexity, the largest V3 (672M) saw +5%, and the smallest V1 (353M) barely moved at +0.2%. The cheapest model was the most robust.

The pattern is selective brittleness. GRPO's effect depends on the model, not on scale. u/john463212's confounds are real: V2 and V3 differ in attention type, data mix, and token count, not just size, which is why "no clean relationship to scale" is the honest finding rather than "GRPO doesn't work."

The stakes are practical. A pipeline inheriting a larger base model is not inheriting a more reliable post-training. The next read is to treat GRPO as a per-model integration cost, not a default upgrade.