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Openrlhf Training

Listed
davila7·vlatest·ai-ml·MIT

OpenRLHF Training enables fine-tuning of large language models using Reinforcement Learning from Human Feedback for improved alignment and helpfulness.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill openrlhf-training -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: 10.0.0.1.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    127.0.0.1local0.0.0.0local10.0.0.1undeclared

    What it found

    • cli-tool/components/skills/ai-research/post-training-openrlhf/references/multi-node-training.md:422contacts 10.0.0.1
      ray job submit --address="http://10.0.0.1:8265" \

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned davila7/claude-code-templates at 79182c51fb.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/post-training-openrlhf/SKILL.md
Scanned
5 files, 1,689 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill facilitates training AI models using Reinforcement Learning from Human Feedback (RLHF). It allows for the iterative refinement of model behavior based on human preferences, leading to improved alignment and performance. The skill provides tools for data collection, reward modeling, and policy optimization within an RLHF pipeline.

When to use it

  • Improving AI Alignment: When you need to ensure your AI's outputs are aligned with specific values or desired behaviors.
  • Refining Conversational Agents: To enhance the quality and relevance of responses from chatbots or other conversational AI systems.
  • Customizing Model Behavior: For scenarios requiring specialized model behavior beyond what’s achievable through standard supervised learning.
  • Iterative Model Development: When you want to continuously improve a model's performance based on ongoing human feedback.

Key capabilities

  • RLHF pipeline support
  • Data collection tools
  • Reward modeling functionality
  • Policy optimization techniques

Example prompts

  • "Train an AI agent using RLHF to summarize articles in a concise and engaging style."
  • "Implement the data collection phase for RLHF training, focusing on preference comparisons between different model outputs."
  • "Optimize the policy of my language model using reinforcement learning from human feedback."

Tips & gotchas

This skill requires familiarity with machine learning concepts and potentially significant computational resources. Successful implementation depends heavily on high-quality human feedback data; insufficient or biased data can negatively impact training results.