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K² · Artificial intelligence

Cheaper RL fine-tuning may be possible — have our ML team test it

The paper conducts a systematic layer-wise study of how reinforcement learning (RL) adaptation is distributed across transformer layers during LLM post-training.

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Zijian Zhang, Rizhen Hu, Athanasios Glentis et al.

7 authors · cs.LG, cs.CL

arXiv preprintArtificial intelligenceJul 2026 · ~45s read

The 30-second scan

A modern AI model is a stack of dozens of processing layers, one feeding into the next.

  1. The authors 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.
  2. The paper introduces a quantity called 'layer contribution' that measures the fraction of full RL improvement recovered by training a layer in isolation.
  3. The study was evaluated across seven models spanning two model families (Qwen3 and Qwen2.5), three RL algorithms (GRPO, GiGPO, Dr. GRPO), and multiple task domains including mathematical reasoning, code generation, and agentic decision-making.
Training a single transformer layer can recover most of the gains achieved by full-parameter RL training, and in some cases even surpass itSeven models were studiedTwo model families (Qwen3, Qwen2.5)
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