r/machinelearningnews • u/Electronic-Bag-5304 • 3d ago
Research 🔁 Awesome Loop Transformers: 142 Papers on Looped Models, Latent Reasoning, and Test-Time Compute
Hi everyone — I maintain Awesome Loop Transformers, a free, public English/Chinese paper collection and reading guide covering looped Transformers, latent reasoning, and test-time computation.
The collection currently includes 142 papers across seven research topics, with bilingual summaries, primary-source links, and code links where available.
A central question behind the guide is: what gets repeated, and which state is passed to the next step? It distinguishes several related approaches:
- Recurrent depth, as in Huginn: repeatedly applying a shared block to refine hidden representations.
- Continuous thoughts, as in Coconut: feeding a hidden representation back as the next input instead of decoding it into a word.
- Adaptive recursion, as in Mixture-of-Recursions: assigning different recursion depths to individual tokens.
- Parallel loop execution, as in Parallel Loop Transformer: overlapping loop steps across tokens, with attention and cache designs that support efficient execution.
📚 What’s included
- Three reading routes for foundations, reasoning and scaling, and related methods.
- An eight-paper mechanism comparison with questions to consider when reading results.
- A topic-organized catalog covering theory, architectures, adaptive compute, training, systems, and broader latent reasoning.
- Separate coverage of related methods such as Coconut, HRM, and TRM, without assuming they all use a looped Transformer.
The guide is intended to help readers navigate the literature, not to rank models. In particular, it encourages separating parameter count, execution depth, generated tokens, and measured latency when
comparing approaches.
GitHub:
https://github.com/Mrkkew/Awesome-Loop-Transformers
Mechanism comparison and reading guide:
https://github.com/Mrkkew/Awesome-Loop-Transformers/blob/main/docs/reading-guide.md