arXiv AI
TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training
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arXiv AI
Jun 29
DataStates-LLM: Scalable Checkpointing for Transformer Models Using Composable State Providers
arXiv:2601. 16956v1 Announce Type: cross Abstract: The rapid growth of Large Transformer-based models, specifically Large Language Models (LLMs), now scaling to trillions of parameters, has necessitated training across thousands of GPUs using complex hybrid parallelism strategies (e.
arXiv AI
Sep 23
Conduit: An Experience Data Plane for Distributed Reinforcement Learning
arXiv:2609.24456v1 Announce Type: cross Abstract: Distributed reinforcement learning (RL) scales training by parallelizing actors and learners around an Experience Buffer. As RL workloads grow, howev...
arXiv AI
Jun 30
Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs
arXiv:2511. 10480v3 Announce Type: replace-cross Abstract: Optimizing the performance of large language models (LLMs) on large-scale AI training and inference systems requires a scalable and expressive mechanism to model distributed workload execution.
arXiv Machine Learning
Sep 14
AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training
AsyncFlow is an asynchronous streaming reinforcement learning framework designed to improve the post‑training phase of large language models. It introduces a distributed data storage and transfer module that enables panoramic data management and fine‑grained scheduling, allowing automated pipeline overlapping and dynamic load balancing. The framework also employs an asynchronous producer‑consumer workflow to reduce computational idleness by deferring parameter updates within staleness thresholds, and it is architecturally decoupled from training and inference engines, providing modular, customizable user interfaces. Experiments show an average throughput improvement of 1.59× over the state‑of‑the‑art baseline.
arXiv Machine Learning
4d ago
Replay the Curvature: Accurate and Scalable NVFP4 Quantization for Large Language Model Inference
arXiv:2609.36654v1 Announce Type: new Abstract: Large language models make weight storage and memory traffic major inference costs, motivating low-precision formats that represent each weight with on...
arXiv AI
Jun 16
RollArt: Disaggregated Multi-Task Agentic RL Training at Scale
arXiv:2512. 22560v2 Announce Type: replace-cross Abstract: Agentic Reinforcement Learning (RL) trains LLMs through multi-turn interactions with environments, producing workloads that mix compute-bound prefill, bandwidth-bound decoding, CPU-heavy environment execution, and bursty reward evaluation.