arXiv 2607.01232 (submitted July 1, updated v2 on July 2) throws out a finding that's quite impactful for the cost structure of LLM post-training: under three mainstream RL algorithms GRPO, GiGPO, and Dr.GRPO, training only a single layer Transformer in Qwen3 / Qwen2.5 models can recover most of the full-parameter RL gain, and on some tasks even surpass full-parameter training. The authors quantify this phenomenon as "layer contribution" — the proportion of full-parameter RL gain that single-layer training can recover. In experiments covering 7 models, spanning three categories of tasks: math reasoning, code generation, Agent decision-making, the "contribution" high layers are highly concentrated in the middle of the Transformer stack, while the layers near input and output ends get almost negligible gain from RL training. More intriguing is that this "middle-concentrated" pattern remains highly stable across datasets, tasks, model families, and RL algorithms — that is, those few layers that truly carry RL adaptation capacity almost always fall in the same relative position. The practical impact of this finding on the post-training pipeline is: the current industry's commonly used full-parameter RL post-training may be "overpaying" to a large extent — most parameter updates can actually be saved, and only fine-tuning the few key layers in the middle is enough to approximate the effect of global RL. If this conclusion is replicated on large models, the cost curve of post-training has the chance of a step-down similar to the pretraining MoE-ization, and the design philosophy of sparsification methods like LoRA / IA³ will also extend from "saving inference memory" to "saving training compute". Of course, the paper also candidly admits: the experiments are mainly done on the Qwen series and 3 algorithms, and the performance on 70B+ models and other algorithms like PPO and DPO still needs follow-up work to verify.