Skills with input "Training configuration"

alterlab-pufferlib - Scale reinforcement learning with PufferLib
reinforcement learning PufferLib PPO vectorized environments
★ 22 · Updated 2026-10-03
Trains reinforcement learning agents with PufferLib using parallel vectorized environments, native multi-agent support, custom PufferEnv tasks, and integrated environments.
⚙ Train PPO agents ⚙ Create PufferEnv tasks ⚙ Vectorize environment simulation
grpo-rl-training - Train language models with GRPO and TRL
Post-training Reinforcement learning GRPO TRL
★ 1 · Updated 2026-10-03
Provides guidance for implementing GRPO reinforcement learning fine-tuning with TRL, custom reward functions, and task-specific training workflows.
⚙ Prepare chat-format datasets ⚙ Define composite reward functions ⚙ Configure GRPO training
fine-tuning-serving-openpi - Fine-Tune and Serve OpenPI Robot Policies
OpenPI Physical Intelligence VLA Robotics
★ 85 · Updated 2026-09-28
Fine-tunes and serves Physical Intelligence OpenPI models with JAX or PyTorch for robot policy inference in ALOHA, DROID, and LIBERO environments.
⚙ Fine-tune OpenPI models ⚙ Serve policy inference ⚙ Convert JAX checkpoints
fine-tuning-with-trl - Post-training and preference alignment for LLMs with TRL
Post-Training TRL Reinforcement Learning Fine-Tuning
★ 0 · Updated 2026-09-24
Provides methods and workflows for LLM post-training, including SFT, DPO, PPO, GRPO, and reward modeling.
⚙ Execute supervised fine-tuning ⚙ Train reward models ⚙ Align models using DPO
numerai-model-implementation - Add new Numerai model types to training pipeline
numerai modeling machine-learning training
★ 1,182 · Updated 2026-02-18
Add a new model type to the Numerai agents training pipeline for training and evaluation
⚙ Register model in model factory ⚙ Handle fit/predict quirks ⚙ Update model configurations
model - Model Development and Fine-Tuning for AI Algorithms
Machine Learning Model Training Fine-tuning Algorithm Development
★ 22 · Updated 2026-02-15
Algorithm and model development skill for dataset design, fine-tuning, evaluation, and deployment
⚙ Design datasets ⚙ Clean datasets ⚙ Perform supervised fine-tuning