Adaptive Layerwise Perturbation: Unifying Off-Policy Corrections for LLM RL

In three linesAdaptive Layerwise Perturbation (ALP) addresses off-policy issues in LLM RL by injecting learnable perturbations into hidden states across all layers. This reduces heavy-tailed importance ratios, stabilizes training, and improves performance on math and multi-turn reasoning tasks while boosting exploration.

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