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layer contribution

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Machine Learning Algorithms & Natural Language Processing

Jul 9, 2026 · Artificial Intelligence

One Layer Is Enough: Single‑Layer RL Beats Full‑Parameter Training Across Models, Tasks, and Algorithms

A systematic study of reinforcement‑learning fine‑tuning for large language models reveals that RL gains are highly concentrated in a few middle Transformer layers, and training just one such layer can match or even exceed full‑parameter RL performance across multiple models, tasks, and algorithms.

Reinforcement Learninglayer contributionmodel training

0 likes · 17 min read

One Layer Is Enough: Single‑Layer RL Beats Full‑Parameter Training Across Models, Tasks, and Algorithms

Machine Heart

Jul 8, 2026 · Artificial Intelligence

One Layer Is Enough: Single‑Layer RL Beats Full‑Parameter Training Across Models, Tasks, and Algorithms

A systematic study of reinforcement‑learning post‑training for large language models shows that most RL gains are concentrated in a few middle Transformer layers, and training just one such layer can match or surpass full‑parameter RL across seven models, three RL algorithms, and multiple task domains, leading to simple yet effective training strategies.

Large Language ModelsRL fine‑tuningReinforcement Learning

0 likes · 17 min read