Experiment Reveals How Small LLMs Break Under Full SWE-bench Task Pipelines

A researcher ran the SWE-bench benchmark on a small language model to study how such models behave under demanding, multi-stage evaluation conditions. The pipeline followed a four-step structure: reasoning, critique, patch generation, and comparison. The goal was not to achieve correct outputs but to identify meaningful failure signals and capability boundaries. Results showed the model could produce genuine reasoning and multi-pass critiques, but collapsed in ways consistent with known small-model limitations. The experiment concluded that the pipeline methodology itself is sound, and the model's capacity — not the evaluation framework — is the primary bottleneck.