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Language models for code optimization: survey, challenges and future directions
Figure 1: A field in taxonomy. LM-based code optimization approaches branch by strategy; the surveyed evidence concentrates where generation meets measurement.∎
A systematic review of more than fifty studies applying language models to code optimization, organized into a taxonomy spanning prompting strategies, fine-tuned optimizers, search-based hybrids, and agentic pipelines.
The survey identifies the field's recurring gap: generation is abundant, validation is scarce. Approaches that couple LM generation with real measurement and correctness checking consistently produce the results that survive scrutiny — the direction the field, and Artemis, are converging on.
Key results
- 50+ studies systematically reviewed and classified
- Identifies measurement-validated generation as the load-bearing pattern
- Roadmap of open challenges for LM-based optimization