Feedback Over Form: Why Execution Feedback Matters More Than Pipeline Topology in 1-3B Code Generation
Small language models (1-3B) are practical to run locally, but individually limited on harder code generation tasks. We ask whether composing them into pipelines can recover some of that lost capability. We study code generation pipelines built from 1-3B models with execution feedback, and use a NEAT-inspired evolutionary search to test whether more complex pipeline structure helps beyond a simple refinement loop. We evaluate on HumanEval (164 problems) and sanitized MBPP (427 problems), all with local inference on a single laptop. Self-refinement with execution feedback improves code generation by more than 4 standard deviations on both benchmarks. The gains are narrow in mechanism: refi...
| Authors | Charles Junichi McAndrews |
|---|---|
| Primary category | cs.SE |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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