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

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.

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

Tevatron Meets Megatron: Expert-Parallel LLM Reranker Training on an Academic Budget

arXiv:2608. 00916v1 Announce Type: cross Abstract: Modern reranking recipes---billion-scale cross-encoders, mixture-of-experts (MoE) backbones, and distillation against strong teachers---have outpaced the training infrastructure available to most academic groups.

Piper: A Programmable Distributed Training System

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.

Policy Gradient with PyTorch

Behind the Scenes of Distributed Training and Why Your GPU Wiring Matters as Much as Your Strategy

A measured look at distributed training, from DDP and FSDP to the ZeRO stages in between, and why the wiring between your GPUs matters as much as the strategy you choose The post Behind the Scenes of Distributed Training and Why Your GPU Wiring Matters as Much as Your Strategy appeared first on Towards Data Science .

Efficient Knowledge Distillation for LLMs: Offline Top-K Logits and a Fused Chunked KL Loss

arXiv:2608. 03796v1 Announce Type: cross Abstract: Small language models are often the only option for deployment under tight latency, cost, and on-premises constraints, but they are rarely trained from scratch: a compressed model is usually recovered through knowledge distillation (KD).

From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning

arXiv:2603. 10263v2 Announce Type: replace-cross Abstract: We introduce Distribution Contractive Reinforcement Learning (DICE-RL), a framework that uses reinforcement learning (RL) as a "distribution contraction" operator to refine pretrained generative robot policies.

OpenAI standardizes on PyTorch

We are standardizing OpenAI’s deep learning framework on PyTorch.

Self-Distilled Policy Gradient

arXiv:2606. 04036v1 Announce Type: new Abstract: On-policy self-distillation, where a language model conditions on privileged context to supervise its own generations, is a promising source of dense supervision for sparse-reward reinforcement learning.