arXiv AI
Oct 2

Looping Beyond Twice: A Scalable Recipe for Looped Mixture-of-Experts

The paper introduces LOOM, a method for scaling looped mixture‑of‑experts (MoE) Transformers beyond the typical two‑loop limit. LOOM addresses two key obstacles: it stabilizes deep recurrence by bounding residual variance and re‑injecting the input embedding, and it prevents expert selection collapse by using per‑loop routers and a looping residual to maintain computational diversity. Experiments on 100 M–1.7 B parameter models show stable scaling to 9–12 loops, with significant perplexity reductions and zero‑shot accuracy gains under near‑iso‑FLOP conditions.

By Di He, Pengxiang Li, Da Chang, Qingyan Meng, Lu Yin, Shiwei Liu
arXiv Computation and Language
5d ago

One Model to Loop Them All: Elastic Latent Recurrence for Language Models

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.

By Faisal Ladhak, Shantanu Acharya, Santiago Akle Serano, Simeng Sun, Boris Ginsburg, Krishna C Puvvada
arXiv AI
2d ago

Virtual Head Attention

Virtual Head Attention (VHA) is a lightweight attention mechanism that improves long‑context large language models by adding two linear operations—Q Premix and Linear Postmix—to recover head diversity and fuse inter‑head features without nonlinearities. VHA reduces KV cache size, training and prefill FLOPs, and maintains or surpasses model quality compared to existing methods such as GQA, MLA, and MFA across multiple scales and benchmarks. The design allows VHA to be folded into standard GQA inference pipelines, enabling efficient deployment with only two KV heads and full exploitation of Tensor Core parallelism.

By Xibo Ding, Guoxia Wang, Jinle Zeng, Jiabin Yang, Sirui Tao, Chao Yang, Dianhai Yu