From PyTorch DDP to Accelerate to Trainer, mastery of distributed training with ease

Accelerating PyTorch distributed fine-tuning with Intel technologies

nanoVLM: The simplest repository to train your VLM in pure PyTorch

WeightBridge: An Efficient Weight Transfer Library for Reinforcement Learning

WeightBridge is a lightweight library that streamlines weight transfer between trainers and rollout generators in reinforcement learning systems, particularly for large language models. It automatically maps trainer and rollout weight layouts, then performs redundancy‑free, load‑balanced transfers while supporting various synchronization modes. Experiments show that WeightBridge can cut GPU stall time by up to 42× compared to leading open‑source RL frameworks, and it was easily integrated into two different frameworks by a coding agent.

Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning

arXiv:2607. 21653v1 Announce Type: new Abstract: Agentic reinforcement learning research is constant algorithm modification, new estimators, new pipeline stages, new rollout schemes, and in mainstream frameworks each change threads through layers of trainer, distributed backend, and rollout glue: the cost lands on the researcher at every iteration.

Miles v0.1: Production-Level Post-Training

arXiv:2609.08368v1 Announce Type: new Abstract: We present Miles v0.1, a full-stack, production-ready system for frontier post-training. Building upon the clean design of slime, Miles designs each st...

Decoupled DiLoCo: A new frontier for resilient, distributed AI training