Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training
Reinforcement learning (RL) has become a central component of post-training large language models (LLMs), yet little is understood about how RL adaptation is distributed across transformer layers. Existing approaches typically update all model parameters uniformly, implicitly assuming that every layer contributes similarly to the gains obtained during RL post-training. In this work, we challenge this assumption through a systematic layer-wise study of RL training. Surprisingly, we find that training a single transformer layer can recover most of the gains achieved by full-parameter RL training, and in some cases even surpass it. To quantify this phenomenon, we introduce the quantity layer...
| Authors | Zijian Zhang, Rizhen Hu, Athanasios Glentis et al. |
|---|---|
| Primary category | cs.LG |
| Published | 1 July 2026 |
| Last updated at source | 2 July 2026 |
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