skills.rest
ReinforceNow
ReinforceNowOFFICIAL
Explore all skills by this author →
WebsiteGitHub

rnow-config

Automate ReinforceNow config.yml and train.jsonl setup for RL, SFT, and Distillation workflows.

31|4|Updated Oct 8, 2025
Search Tags:#training#configure#reinforcement-learning#rnow#config.yml#train.jsonl#dataset-conversion
One-click install
npx skills add https://github.com/ReinforceNow/reinforcenow-cli --skill rnow-config
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rnow-config
Source: https://github.com/ReinforceNow/reinforcenow-cli/tree/main/project8/.claude/skills/rnow-config
Command: npx skills add https://github.com/ReinforceNow/reinforcenow-cli --skill rnow-config

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams configure ReinforceNow RL, SFT, and Distillation training pipelines by providing clear config.yml and train.jsonl guidance, along with dataset conversion support.

Core Features & Use Cases

  • Guided project structure: Show where config.yml, train.jsonl, rewards.py, and tools.py live and how to organize files.
  • Config templates: Provide minimal RL, SFT, and Distillation YAML examples to accelerate setup.
  • Dataset conversion: Walkthrough converting HuggingFace datasets to the train.jsonl format used by ReinforceNow.
  • Validation & examples: Notes on common validation rules, run routes, and testing steps.

Quick Start

Install ReinforceNow tooling, initialize a project with rnow init, and follow the minimal config.yml and train.jsonl examples to start a training run.

Frequently Asked Questions about rnow-config

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I configure a reinforcement learning training pipeline with config.yml and train.jsonl?▼

Structure your reinforcement learning project by placing config.yml, train.jsonl, rewards.py, and tools.py in the designated directories. This guided organization standardizes setup and ensures clearly defined data paths for training.

What is the best way to convert a HuggingFace dataset for reinforcement learning training?▼

Convert HuggingFace datasets by transforming them into the train.jsonl format required by the training pipeline. This dataset conversion process ensures your data aligns with the standardized structure needed for successful runs.

Can I use the same configuration structure for SFT and distillation workflows?▼

Yes, the same configuration structure supports SFT and distillation workflows alongside reinforcement learning. Minimal YAML templates are provided for each workflow type to accelerate setup and maintain consistency across different training modes.

How do I validate my project structure before starting a training run?▼

Validate your project structure by applying common validation rules to config.yml and train.jsonl before execution. This process checks that data paths, model sections, and trainer configurations meet the required standards for a successful run.

Why does my training configuration fail when setting up rollout parameters?▼

Training configuration fails when rollout parameters are not properly defined in the config.yml file. Using the provided minimal templates ensures common rollout configurations are correctly structured and validated before execution.

Related Skills

Explore More Software Engineering Skills→
ReinforceNowReinforceNow
31

rnow-cli

Automate RLHF training workflows via the rnow command-line interface.

Official
Advanced
huggingfacehuggingface
10.9k

hugging-face-model-trainer

Train and fine-tune language models on Hugging Face Jobs using TRL methods.

Official
Advanced
ovachieverovachiever
52

training-llms-megatron

Train large language models with Megatron-Core using configurable 3D and 4D parallelism.

Community
Advanced
icyzhicyzh
1

fine-tuning-with-trl

Post-train language models with SFT, DPO, RLOO, and GRPO using HuggingFace libraries.

Community
Advanced