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
- Same Echo Maze prompt, three frontier models: all passed visually but shipped the same hidden bug — eyishazyer · 2026-09-11
- Benchmark scores drop from 89% to 19% on new evals — how benchmaxxing breaks leaderboard trust — airesearch12 · 2026-09-11
- ChatGPT tells user their question is too hard and to 'accept dumber answers' — phido3000 · 2026-09-11
- Developer Building a Unified Leaderboard of All Model Benchmark Scores — airesearch12 · 2026-09-11
- Rumor claims Kimi faked performance by serving Claude; DeepSeek new model surprises in evals — realsohamparekh · 2026-09-11
- GPT-5.6 writes well but is instantly forgettable, user complains — BasedRaddka · 2026-09-11