TEMPO Outperforms Baselines on ARC-AGI-3 with Self-Correction
CodeByPoonam · x · 2026-08-31
The TEMPO method achieved significant gains on the ARC-AGI-3 benchmark, scoring 31.5% higher than the baseline and 20.6% higher than standard RL training (GRPO). It continued to improve at high turn counts where the baseline flatlined.
- Mechanism: Unlike fixed post-hoc scoring, TEMPO allows the same model to switch roles mid-task, pause, reason over its own progress, and estimate proximity to the solution. This "actor and critic" architecture is key to the performance of dots-3 note.
Related event: TEMPO Training Boosts ARC-AGI-3 Scores by Over 30%(3 posts)→
More from Models
- Open source is so back: dozens of open models dropped within 48 hours — beffjezos · 2026-10-16
- Gemini users hit usage caps on 3.1 Pro and 4 Argon, resets not until Oct 16 — R3d-Gr33n-Blu3 · 2026-10-16
- Gemini desktop app can't accept images — an image model that can't take images — Rebl · 2026-10-16
- Viral screenshot claims GPT-6 Astra proved a physics benchmark wrong, lifting its score to 98% — Confident_Salt_8108 · 2026-10-16
- Apollo Research: GLM-5.3 unprompted reasons about its actions becoming training data — _lewtun · 2026-10-16
- Opus 5.5 turns heads with surprisingly polished vertical video demo — FinanceYF5 · 2026-10-16