aiqbench analysis
LooM introduces a novel architecture for causal encoder-decoder models that decouples memory usage from computational depth by reusing global KV caches across loop iterations. This approach allows dynamic adjustment of model capacity at deployment without increasing cache size, directly addressing key bottlenecks in scaling transformer-based language models. The research demonstrates significant gains in zero-shot accuracy and long-context performance while maintaining efficient resource utilization.
LooM: Loop-Invariant Memory for Causal Encoder-Decoder Language Models
arXiv:2611.06835v1 Announce Type: new Abstract: Looped Transformers increase effective depth by re-executing shared layers, but three limitations hinder their adoption: memory grows with the loop count, compute can only be adjusted in whole-block steps, and the loop count is hard to change after training. We propose LooM (Loop-invariant Memory), which addresses each in turn. In a causal encoder-decoder (CED), LooM loops only the decoder layers after the first, which read a global memory written once per token, and each of them publishes the key-value (KV) entry for a token only on its first visit, so the cache does not grow with the loop count. These layers loop independently, providing every budget from 3 to 21 layer executions per token, and one model trained on randomly sampled budgets covers all of them without a router. With enough loops, LooM outperforms a single-pass baseline trained on the same data on language modeling, zero-shot accuracy and long-context perplexity, while using the same cache size. Our analysis further shows that the training budget distribution decides what extra depth buys. These results suggest that a CED is a natural place to loop: memory is written once, and depth becomes a budget chosen at deployment.
Abstract:Looped Transformers increase effective depth by re-executing shared layers, but three limitations hinder their adoption: memory grows with the loop count, compute can only be adjusted in whole-block steps, and the loop count is hard to change after training. We propose LooM (Loop-invariant Memory), which addresses each in turn. In a causal encoder-decoder (CED), LooM loops only the decoder layers after the first, which read a global memory written once per token, and each of them publishes the key-value (KV) entry for a token only on its first visit, so the cache does not grow with the loop count. These layers loop independently, providing every budget from 3 to 21 layer executions per token, and one model trained on randomly sampled budgets covers all of them without a router. With enough loops, LooM outperforms a single-pass baseline trained on the same data on language modeling, zero-shot accuracy and long-context perplexity, while using the same cache size. Our analysis further shows that the training budget distribution decides what extra depth buys. These results suggest that a CED is a natural place to loop: memory is written once, and depth becomes a budget chosen at deployment.
Computation and Language (cs.CL)
arXiv:2611.06835 [cs.CL]
(or arXiv:2611.06835v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.06835 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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