Title: SuperCoder: Assembly Program Superoptimization with Large Language Models
Primary area: datasets and benchmarks
Keywords: Large language model, Program Optimization

Abstract: Superoptimization is the task of transforming a program into a faster one while preserving its input–output behavior. In this work, we investigate whether large language models (LLMs) can serve as superoptimizers, generating assembly programs that outperform code already optimized by industry-standard compilers. We construct the first large-scale benchmark for this problem, consisting of 8,072 real-world assembly programs averaging 130 lines, in contrast to prior datasets restricted to 2–15 straight-line, loop-free programs. We evaluate 23 LLMs on this benchmark and find that the strongest baseline, Claude-opus-4, achieves a 51.5% test-passing rate and a 1.43× average speedup over gcc -O3. To further enhance performance, we fine-tune models with reinforcement learning, optimizing a reward function that integrates correctness and performance speedup. Starting from Qwen2.5-Coder-7B-Instruct (61.4% correctness, 1.10× speedup), the fine-tuned model SuperCoder attains 95.0% correctness and 1.46× average speedup. Our results demonstrate, for the first time, that LLMs can be applied as superoptimizers for assembly programs, establishing a foundation for future research in program performance optimization beyond compiler heuristics.