# 单层Transformer即可匹配全参数强化学习训练：Qwen3/Qwen2.5等模型研究

- 来源：Hacker News 热门（buzzing.cc 中文翻译）
- 作者：tcp_handshaker
- 发布时间：2026-07-03 02:32
- AIHOT 分数：68
- AIHOT 链接：https://aihot.virxact.com/items/cmr3usrgm00aoslbrmhwjkn73
- 原文链接：https://arxiv.org/abs/2607.01232

## AI 摘要

研究发现，训练单个Transformer层即可恢复甚至超越全参数强化学习（RL）后训练带来的收益。研究引入“层贡献度”量化指标，在Qwen3和Qwen2.5两个模型家族的七个模型上，使用GRPO、GiGPO、Dr. GRPO三种RL算法，覆盖数学推理、代码生成和智能体决策任务，发现RL收益高度集中于少数Transformer层，且高贡献层集中在堆栈中间，两端层贡献显著较小。

## 正文

Computer Science > Machine Learning

[Submitted on 1 Jul 2026]

Title:Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training

Authors:Zijian Zhang, Rizhen Hu, Athanasios Glentis, Dawei Li, Chung-Yiu Yau, Hongzhou Lin, Mingyi Hong

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Abstract: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 contribution, which measures the fraction of full RL improvement recovered by training a layer in isolation. Across seven models spanning two model families (Qwen3, Qwen2.5), three RL algorithms (GRPO, GiGPO, Dr. GRPO), and multiple task domains including mathematical reasoning, code generation, and agentic decision-making, we observe a remarkably stable pattern: RL gains are highly concentrated in a small subset of, and in many cases even a single, transformer layers. More strikingly, the same structural pattern consistently emerges: high-contribution layers concentrate in the middle of the transformer stack, while layers near the input and output ends contribute substantially less. The resulting layer rankings remain strongly correlated across datasets, tasks, model families, and RL algorithms.

Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL)

Cite as: arXiv:2607.01232 [cs.LG]

(or arXiv:2607.01232v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.01232

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zijian Zhang [view email]
[v1] Wed, 1 Jul 2026 17:59:54 UTC (268 KB)

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