slime-rl-training
🎯Skillfrom orchestra-research/ai-research-skills
Trains reinforcement learning agents using slime-based simulation environments with advanced exploration and policy optimization techniques
Part of
orchestra-research/ai-research-skills(84 items)
Installation
npx @orchestra-research/ai-research-skillsnpx @orchestra-research/ai-research-skills list # View installed skillsnpx @orchestra-research/ai-research-skills update # Update installed skills/plugin marketplace add orchestra-research/AI-research-SKILLs/plugin install fine-tuning@ai-research-skills # Axolotl, LLaMA-Factory, PEFT, Unsloth+ 4 more commands
More from this repository10
Streamlines AI research workflows by providing curated Claude skills for data analysis, literature review, experiment design, and research paper generation.
Assists AI researchers in drafting, structuring, and generating machine learning research papers with academic writing best practices and technical precision.
Streamlines distributed data processing and machine learning workflows using Ray's scalable data loading and transformation capabilities.
Streamlines distributed machine learning training using Ray, optimizing hyperparameter tuning and parallel model execution across compute clusters.
Accelerates AI model inference by predicting and parallel processing multiple token candidates to reduce latency and improve generation speed.
Generates structured document outlines and hierarchical content maps with customizable depth and formatting for research and writing workflows
Automates scientific literature curation by extracting, summarizing, and organizing research papers from marine biology and oceanography domains
Provision and manage GPU cloud instances on Lambda Labs for machine learning and AI research workloads with automated setup and configuration.
Trains large language models using open-source reinforcement learning from human feedback (RLHF) techniques with advanced alignment and reward modeling
Trains sparse autoencoders on neural network activations to discover interpretable features and understand internal representations