The paper introduces CHASE, a cache‑hole‑adapted skip‑exit mechanism for looped state‑space language models, specifically Looped Mamba and Looped Hybrid Mamba‑Transformer. It shows that looping these architectures improves performance on controlled reasoning tasks and remains competitive in pre‑training benchmarks while using fewer distinct parameters. The cache‑hole adaptation allows selective skipping of recurrent steps during inference, maintaining perplexity close to full computation and achieving significant speedups.
arXiv:2607. 10110v1 Announce Type: new Abstract: Recent work on looped language models suggests that many reasoning problems benefit from greater computational depth rather than from additional independent parameters.
arXiv:2609.36636v1 Announce Type: new Abstract: Looped language models (LoopLMs) increase computational depth through parameter sharing, offering a path to scale inference computation without adding...
The paper presents practical training recipes for looped language models, showing that a compute‑efficient pipeline can reduce the training budget from 7.7 T tokens to 310 B tokens while maintaining strong reasoning performance. In controlled experiments, a 1.4 B LoopLM outperforms a parameter‑matched dense model on 12 benchmarks, achieving significant gains on GSM8K, MATH, and DROP, and approaches a 3.9 B dense model at matched inference compute. The authors also provide a minimal recipe to convert pretrained dense models into looped ones, demonstrating improvements on Qwen3‑1.7B‑Base across multiple data regimes.
The paper introduces Elastic Latent Recurrence (ELR), a training recipe that enables looped language models to match the accuracy of non-recurrent models at a single loop while allowing additional loops to improve accuracy with more compute. ELR achieves this through gated state updates with learned step sizes, mixed loop counts during training, and conditioning on loop index and budget. The method also reduces training and inference overhead in Mixture-of-Experts models and produces a single checkpoint, Koi‑4B, that performs competitively with state‑of‑the‑art models on multiple benchmarks.
arXiv:2603.21676v2 Announce Type: replace-cross Abstract: Standard Transformers have a fixed computational depth, limiting their ability to generalize to tasks that require variable-depth reasoning....
arXiv:2607. 00341v1 Announce Type: cross Abstract: Large language models achieve strong performance on many reasoning tasks when allowed to externalize intermediate steps as Chain-of-Thought (CoT).
The paper investigates looped language models that reuse Transformer blocks across logical depth, separating recurrence placement, input injection, and recurrent state design. Experiments at matched parameter scale, logical depth, and token budgets show that a prelude‑recurrent‑block‑coda architecture improves context extraction and reasoning, while input injection boosts knowledge retrieval. The Huginn‑style design, especially at 730 M parameters and 336 B tokens, outperforms the Ouro architecture on all ten benchmarks, and its advantages transfer to Mixture‑of‑Experts models, achieving superior performance with fewer resident parameters.
arXiv:2609.33149v2 Announce Type: replace Abstract: A common principle of effective learning is to practice material that is neither already mastered nor too difficult to permit progress. We ask how...
arXiv:2609.15160v1 Announce Type: new Abstract: Looped Transformers have recently demonstrated strong performance in both reasoning and language tasks by reusing a shared set of parameters across mul...
The Asymmetric Looped Transformer (AsymLoop) separates context encoding from state refinement by using a single-pass causal encoder to produce fixed global key-value caches and a weight-tied recurrent decoder to generate local key-values from its evolving state. Joint attention over both sources allows each update to combine persistent contextual information with the current intermediate state. Experiments on FineWeb-Edu across models from 60M to 1B show lower validation perplexity and higher downstream accuracy than full-loop models, with a 1B model improving zero-shot commonsense reasoning accuracy from 49.58% to 50.85%.
The paper introduces LoopCD, a training‑free contrastive decoding framework that improves token selection in Loop‑Transformer models by comparing the final prediction with earlier recurrent passes. LoopCD operates either in logit space (LoopCD‑Logits) with a single extra output pass or in hidden‑state space (LoopCD‑Hidden) with no output overhead. Across multiple looped Transformer families, LoopCD yields significant performance gains—raising pass@1 scores on tasks such as AIME 2024 and HumanEval—while enabling a reduction in the number of recurrent loops and a corresponding decrease in inference FLOPs.
The paper introduces Gated Recurrent Transformers, a depth‑sharing architecture that brackets a single shared core with fixed prelude and coda blocks and uses a lightweight projection and element‑wise update gate to modulate recurrent updates. This design allows functional specialization across recurrences while reducing memory footprint. Experiments show that, under equal FLOPs or parameter budgets, the recurrent model matches or surpasses deeper GPT‑2 Small baselines, achieving similar or better accuracy with fewer parameters and lower peak decoding memory.