Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning

arXiv:2607.04577v1 Announce Type: new Abstract: Code models strictly prioritize functional correctness, leaving software energy efficiency as an unoptimized byproduct. Training models to generate energy-efficient code requires reproducible feedback at scale, which physical hardware measurement cannot reliably provide due to variance. In this paper, we replace hardware profiling with a deterministic architectural simulation harness to build Green Tea, a corpus of 3.5 million evaluations across 1{,}474 C++ problems. We train an energy-aware code model via supervised fine-tuning on energy-contrastive pairs, followed by closed-loop reinforcement learning (GRPO) using simulation-in-the-loop feedback. To rigorously evaluate deployment readiness, we introduce the Correctness-Adjusted Reduction in Energy Total (CARET), a metric that explicitly penalizes code that sacrifices functionality for efficiency. On 143 held-out problems, our simulation-in-the-loop pipeline achieves 12.63% CARET, nearly tripling the gain of fine-tuning alone, and successfully beats the energy efficiency of human-expert references on 58.4% of its valid outputs. Furthermore, our analysis exposes the IPC trap: standard throughput proxies like Instructions-Per-Cycle (IPC) actively misrank true energy efficiency on 67.8% of problems, proving the absolute necessity of direct energy simulation. By releasing our dataset and infrastructure, we bypass the 263{,}000 CPU-hours required for reproduction, structurally empowering the community to deploy inherently energy-efficient code generation models.

Source: arXiv cs.LG | 2026-07-07