State-of-the-art large language model (LLM) training takes tens of thousands of graphics processing units (GPUs) for months and encounters failures across the software and hardware stack. Existing fault-tolerance mechanisms either impose non-trivial overhead during failure-free execution or suffer from prolonged recovery latency, particularly under scenarios where a small subset of compute nodes experience permanent failures.
arXiv:2607. 01646v1 Announce Type: new Abstract: State-of-the-art large language model (LLM) training takes tens of thousands of graphics processing units (GPUs) for months and encounters failures across the software and hardware stack.
By Haotian Xie, Junlin Chen, Mingkai Zheng, Lishan Yang, Zhao Zhang
arXiv:2608. 14635v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly trained with reinforcement learning in long-horizon, sandboxed environments.
By Jiecheng Zhou, Qinghao Hu, Peng Sun, Xingcheng Zhang, Weiming Zhang
Leto is a fault‑tolerant training system for large language models that enables fast in‑place recovery on surviving hardware after hardware‑operable failures. It retains the working model state and reusable process state, while pre‑initializing remaining state in a shadow trainer, using two‑tier erasure protection and chunk‑level transactional updates to maintain consistency. Experiments on NVIDIA A100 clusters show Leto recovers 3.6–6.5× faster than checkpointing baselines and boosts productive training time by up to 13.7 percentage points, with simulations indicating over 95% productivity on a 131,072‑GPU cluster.
By Geon-Woo Kim, Joon Ha Kim, Daehyeok Kim
Long-running LLM agents keep valuable state resident on GPUs: KV caches, request schedulers, communication state, and sometimes online adapters. Losing this state after a GPU or communicator failure can discard minutes to hours of work, yet existing recovery mechanisms either restart the whole serving stack or require application-specific checkpoint logic inside every attention and runtime component.
arXiv:2606. 20537v1 Announce Type: new Abstract: Mainstream LLM serving systems reuse prefix work mainly through paged or radix key-value (KV) caches.
By Liang Su