Afterburner: Reinforcement Learning Facilitates Self-Improving Code
  Efficiency Optimization
Authors: name: Mingzhe Du, name: Luu Tuan Tuan, name: Yue Liu, name: Yuhao Qing, name: Dong Huang, name: Xinyi He, name: Qian Liu, name: Zejun Ma, name: See-kiong Ng

Large Language Models (LLMs) generate functionally correct solutions but
often fall short in code efficiency, a critical bottleneck for real-world
deployment. In this paper, we introduce a novel test-time iterative
optimization framework to address this, employing a closed-loop system where
LLMs iteratively refine code based on empirical performance feedback from an
execution sandbox. We explore three training strategies: Supervised Fine-Tuning
(SFT), Direct Preference Optimization (DPO), and Group Relative Policy
Optimization~(GRPO). Experiments on our Venus dataset and the APPS benchmark
show that SFT and DPO rapidly saturate in efficiency gains. In contrast, GRPO,
using reinforcement learning (RL) with execution feedback, continuously
optimizes code performance, significantly boosting both pass@1 (from 47% to
62%) and the likelihood of outperforming human submissions in efficiency (from
31% to 45%). Our work demonstrates effective test-time code efficiency
improvement and critically reveals the power of RL in teaching LLMs to truly
self-improve code efficiency.