Study: Single Transformer Layer Captures Most RL Post-Training Gains
rohanpaul_ai · x · 2026-07-03
A new paper tested an unconventional hypothesis: freezing the rest of a model and training only a single Transformer layer for RL post-training. The results revealed that this single layer often recovers a significant proportion of the complete RL improvements. The study proposes a "layer contribution" metric, revealing that effective changes from RL post-training are more akin to "layer selection" rather than whole-model learning. This finding is crucial for understanding RL post-training mechanisms and offers new avenues for efficient post-training methods.
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