Research & models Oct 22, 2026

Trend · preprint October 17

Arctic-SWE lifts Qwen3.5-4B to 62.0%; score is self-reported

Snowflake AI Research's Arctic-SWE, with no new algorithm, trains the 4-billion-parameter Qwen3.5-4B to a 62.0% solve rate on the coding benchmark SWE-bench Verified, 0.5 points above the FrogNano baseline the authors measured, as the paper self-reports. SWE-bench Verified is a coding benchmark built from real GitHub issues. The authors write: "On raw R2E-Gym data, without new algorithms or data work, Arctic-SWE trains Qwen3.5-4B to 62.0% pass@1 on SWE-bench Verified" (arXiv 2610.24914, 2026-10-17). pass@1 is the solve rate with a single attempt per problem. R2E-Gym is an existing public training environment. The baseline, FrogNano, also synthesizes tasks online and scores 61.5%. The scores differ by only 0.5 percentage points, and Arctic-SWE uses about one-sixth of FrogNano's training samples. With data filtering, the 4B model's Verified score reaches 65.0%. This is a single self-reported paper, posted on October 17, and any independent replication falls outside this issue's sources. Scores move with data filtering and test strength. Our reading: further gains for small models on this benchmark may hinge on the reinforcement-learning system and data quality rather than algorithms. This item doesn't compare against the strongest current models' scores on the same benchmark.