Single Transformer layer training can match full-parameter RL, paper says
RisingSayak · x · 2026-07-21
A new paper argues that reinforcement learning gains in Transformers may come disproportionately from only a few layers.
- The authors test whether training a single Transformer layer can match full-parameter RL training.
- Across seven models, they find a recurring pattern: layers around 40%–60% of depth contribute the most.
- In the figure shown, selectively training the most useful layers can match or surpass full RL training on math benchmarks, sometimes with fewer trainable parameters and better performance than full-parameter RL.
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