arXiv:2606. 11169v1 Announce Type: cross Abstract: Large-scale model training increasingly relies on composing multiple parallelism strategies, such as data, pipeline, and expert parallelism, together with memory-saving optimizations like ZeRO.
arXiv:2605.21603v1 Announce Type: cross Abstract: Intra-device parallelism addresses resource under-utilization in ML inference and training by overlapping the execution of operators with different r...
arXiv:2609.39350v1 Announce Type: cross Abstract: As model sizes continue to scale, distributed training has become inevitable. Automatic parallelization techniques can derive efficient training para...
arXiv:2607. 20145v1 Announce Type: cross Abstract: Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pressure, non-overlapped communication overhead, and inefficient kernel execution.
As model sizes continue to scale, distributed training has become inevitable. Automatic parallelization techniques can derive efficient training parallelism strategies at low cost while achieving supe...
arXiv:2602. 21788v2 Announce Type: replace-cross Abstract: Scaling long-context capabilities is crucial for Large Language Models (LLMs).
arXiv:2608. 07524v1 Announce Type: new Abstract: Training deep learning models on variable long sequences poses significant computational challenges.
The paper "Performance Foundations of Parallel & Distributed Reasoning Language Models" examines how reinforcement learning with verifiable rewards (RLVR) and similar post‑training methods improve reasoning in large language models, yet demand massive computational resources. It provides a compute‑centric analysis of key RL frameworks such as PPO and GRPO, and introduces a taxonomy of intra‑ and inter‑model parallelism strategies—including data, tensor, pipeline, sequence, context, expert, disaggregated placement, stage fusion, hybrid parallelism, and asynchronous execution—to address the parallel and distributed systems challenges of training reasoning language models. The authors also analyze existing RLM frameworks, offering practical guidelines and outlining open research directions for building scalable, fast, and cost‑effective RLMs.
The paper examines the computational challenges of training Reasoning Language Models (RLMs) using reinforcement learning with verifiable rewards (RLVR) and similar post‑training methods. It provides a compute‑centric analysis of popular RL algorithms such as PPO and GRPO, and introduces a taxonomy of intra‑ and inter‑model parallelism strategies—including traditional and novel techniques—to improve scalability and cost‑efficiency. The authors also evaluate existing RLM frameworks and offer practical guidelines and research directions for building high‑performance, scalable RLMs.
arXiv:2604. 26256v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time -- is bottlenecked by skewed generation: long-tailed trajectories indispensable for model performance block the entire training pipeline.
arXiv:2509.21275v5 Announce Type: replace-cross Abstract: Long context training is crucial for extending LLM context windows. Existing schemes, such as sequence parallelism, incur substantial communi...
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...
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.