LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation
Post-training large language models (LLMs) via reinforcement learning (RL) has significantly advanced code generation capabilities. To bypass the heavy memory footprint of critic networks, current state-of-the-art frameworks leverage critic-free paradigms like Group Relative Policy Optimization (GRPO) tied to rule-based verification sandboxes. However, applying these frameworks to low-level systems programming, such as CUDA kernel generation-presents severe challenges: binary pass/fail rewards i
Record details
Published: 3 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Regulation · Environment
Retrieved: 5 August 2026
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ethics.ai (3 August 2026), “LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation,” evidence record 16583, https://ethics.ai/record/16583 (originally published by arXiv cs.LG).
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