Memory-Efficient Looped Transformer: Decoupling Compute from Memory in Looped Language Models

TL;DR

MELT introduces a memory-efficient transformer architecture that decouples reasoning depth from memory use, enabling scalable iterative reasoning in language models without increasing memory footprint.

Contribution

The paper proposes MELT, a novel architecture with shared KV caches and a training method that enables constant-memory iterative reasoning in language models.

Findings

MELT outperforms standard LLMs of similar size after fine-tuning.

MELT maintains a memory footprint comparable to standard models, much smaller than Ouro.

MELT achieves scalable reasoning without sacrificing performance.

Abstract

Recurrent LLM architectures have emerged as a promising approach for improving reasoning, as they enable multi-step computation in the embedding space without generating intermediate tokens. Models such as Ouro perform reasoning by iteratively updating internal representations while retaining a standard Key-Value (KV) cache across iterations, causing memory consumption to grow linearly with reasoning depth. Consequently, increasing the number of reasoning iterations can lead to prohibitive memory usage, limiting the practical scalability of such architectures. In this work, we propose Memory-Efficient Looped Transformer (MELT), a novel architecture that decouples reasoning depth from memory consumption. Instead of using a standard KV cache per layer and loop, MELT maintains a single KV cache per layer that is shared across reasoning loops. This cache is updated over time via a learnable gating mechanism. To enable stable and efficient training under this architecture, we propose to train MELT using chunk-wise training in a two phase procedure: interpolated transition, followed by attention-aligned distillation, both from the LoopLM starting model to MELT. Empirically, we show that MELT models fine-tuned from pretrained Ouro parameters outperform standard LLMs of comparable size, while maintaining a memory footprint comparable to those models and dramatically smaller than Ouro's. Overall, MELT achieves constant-memory iterative reasoning without sacrificing LoopLM performance, using only a lightweight post-training procedure.