Loopie Recurrent Transformer Outperforms Traditional Architectures at Equal Compute
Zitian Gao · hf · 2026-07-20
The Loopie series of recurrent Transformer models has been released, featuring two MoE versions: 20B total params (2B activated) and 6B total params (0.6B activated).
- Addressing Bottlenecks: Previously, increasing pre-training compute for recurrent Transformers was often less effective than simply scaling up parameters; Loopie overcomes this limitation.
- Performance: Ablation studies show that under an identical compute budget, its performance significantly exceeds traditional 30B-A3B baseline models.
- Reasoning Capabilities: Empowered by a novel post-training pipeline, the model achieves gold-medal-level performance in the 2025 IMO (International Mathematical Olympiad) and IPhO (International Physics Olympiad) without relying on external tools.
Related event: Loopie Looping Transformers Match Larger Models at Fraction of Cost(7 posts)→
More from Models
- GPT-6.1 Terra lands: early users praise speed, value and subagent performance — BenBajarin · 2026-10-27
- Leaked GPT-6.1 Terra benchmarks: cheap but 'boring', still far behind Astra and Claude — Angaisb_ · 2026-10-27
- Artificial Analysis breaks down all sub-evaluations in Intelligence Index v4.3.2 — ArtificialAnlys · 2026-10-27
- GPT-6.1 Terra gains ~20 points on Terminal-Bench 4.0, rivalling GPT-6.1 Sol — ArtificialAnlys · 2026-10-27
- GPT-6.1 Terra cuts hallucination rate by 50 points while lifting accuracy 2 points — ArtificialAnlys · 2026-10-27
- GPT-6.1 Terra scores 48 on AA Coding Agent Index at one-third the cost of GPT-5.6 Terra — ArtificialAnlys · 2026-10-27