SecRL-Prune learns layer-wise pruning policies via RL on CodeLLMs, preserving higher pass@k and var@k than baselines at 10-30% compression on HumanEval and enabling semantics-preserving mutations that reduce malware detections in a case study.
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SecRL-Prune: Structured Reinforcement Learning-Based Pruning of CodeLLMs for Preserving Adversarial Code Mutation
SecRL-Prune learns layer-wise pruning policies via RL on CodeLLMs, preserving higher pass@k and var@k than baselines at 10-30% compression on HumanEval and enabling semantics-preserving mutations that reduce malware detections in a case study.