CAT — MMLU zero-shot — SFT-tu: leaderboard
Appendix A.6 Table 4, method=SFT-tu. Base means unmodified Llama-3.3-70B-Instruct or Qwen3.6-27B with reasoning OFF, not pretrained base. MMLU: all 57 subjects, n=14,042, zero-shot. MT-Bench: 80 two-turn questions, Gemma-4-31B-it judge, 1–10. Prompt/steering conditions remain separate boards; SFT/CAPO/CATCH learned adapters are source-qualified distinct model identities and never merged into the original checkpoint. Their public weight release is unverified. Metric: Accuracy (%). Higher is better. Source: https://arxiv.org/html/2610.22292v1
Metric: Accuracy (%). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 2 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Qwen3.6 27B (Non-reasoning) (SFT-tu, CAT 2610.22292) | 84.5 | |
| 2 | Llama 3.3 70B Instruct (SFT-tu, CAT 2610.22292) | 82.2 |
Interactive version: theaggregate.ai/benchmark?slug=cat-mmlu-zero-shot-sft-tu · How It Works · Data refreshed daily, snapshot 2026-10-17.