LogoClawIndex
CasesSkillsAbout
LogoClawIndex

ClawIndex

OpenClaw Skills & Use Case Index

ClawIndex is an ecosystem-driven index of OpenClaw skills and real-world use cases.

Index

Skills·
Cases

Meta

About·
Disclaimer·
Email·
GitHub
© 2026 ClawIndex All Rights Reserved.

Skills with output: training metrics

Browse skills that produce this output.

  • verl-rl-training - RL Training Library for LLMs
    Reinforcement LearningRLHFGRPOPPO

    ★ 650 · Updated 2026-06-30

    Provides guidance for training LLMs with RL using verl library

    ⚙ train models with RLHF⚙ train models with GRPO⚙ train models with PPO
  • verl-rl-training - LLM Reinforcement Learning Training with Verl
    Reinforcement LearningRLHFPost-TrainingDistributed Training

    ★ 0 · Updated 2026-06-30

    Train LLMs at scale using verl with PPO, GRPO, and other RL algorithms

    ⚙ train models with PPO⚙ train models with GRPO⚙ configure RL algorithms
  • verl-rl-training - Train LLMs with verl RL library
    Reinforcement LearningRLHFModel TrainingDistributed Computing

    ★ 1 · Updated 2026-06-30

    Flexible RL training library for large language models supporting multiple algorithms and backends

    ⚙ train LLM with GRPO algorithm⚙ train LLM with PPO algorithm⚙ train LLM with RLOO algorithm
  • pytorch-lightning - PyTorch Lightning Training Framework
    PyTorch Lightningtraining frameworkdistributed trainingddp

    ★ 2 · Updated 2026-05-28

    High-level PyTorch framework for distributed training with automatic DDP/FSDP/DeepSpeed support

    ⚙ train model⚙ validate model⚙ test model
  • PyTorch ML - PyTorch Deep Learning Environment
    deep learningmachine learningGPU accelerationcomputer vision

    ★ 15 · Updated 2026-03-21

    Complete machine learning environment with PyTorch for model training and GPU acceleration

    ⚙ define neural network⚙ load pretrained model⚙ train model
  • distributed-training - Distributed Training Parallelism Strategies
    machine learningdistributed trainingGPU paralleldeep learning

    ★ 2 · Updated 2026-03-10

    Provides guidance for scaling ML training across multiple GPUs and nodes with parallelism strategies

    ⚙ train across multiple GPUs⚙ select parallelism strategy⚙ synchronize gradients