Adaptive Layerwise Perturbation: Unifying Off-Policy Corrections for LLM RL
Off-policy problems such as policy staleness and training--inference mismatch have become a major bottleneck for training stability and further exploration in LLM RL. The distribution gap between the inference and updated policies grows because of the techniques to enhance inference efficiency, leading to heavy-tailed importance ratios. Heavy-tailed ratios arise when the policy is locally sharp, which further inflates gradients and can push updates outside the trust region. To address this, we propose Adaptive Layerwise Perturbation (ALP), which injects small learnable perturbations into the input hidden states of each layer during updates and uses the resulting perturbed policy as the nu...
| Authors | Chenlu Ye, Xuanchang Zhang, Yifan Hao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 March 2026 |
| Last updated at source | 15 May 2026 |
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