Grpo Rl Training
Skill ActiveExpert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
To empower users to fine-tune language models for specific tasks like enforcing output formats, teaching verifiable tasks, improving reasoning, and aligning models to domain-specific behaviors, particularly when custom reward signals are needed.
Features
- Expert guidance on GRPO algorithm fundamentals
- Detailed implementation workflow (dataset, rewards, training, deployment)
- Production-ready training templates and code examples
- Extensive library of customizable reward functions
- Hyperparameter tuning advice and troubleshooting guide
Use Cases
- Enforcing specific output formats (XML, JSON)
- Teaching verifiable tasks with objective correctness metrics
- Improving reasoning capabilities through chain-of-thought rewards
- Aligning models to domain-specific behaviors without preference data
Non-Goals
- Simple supervised fine-tuning tasks
- Tasks without clear reward signals
- Scenarios where high-quality preference pairs are already available (DPO/PPO are better)
Trust
- warning:Issues AttentionIn the last 90 days, 17 issues were opened and 4 were closed, indicating a low closure rate and potentially slow maintainer response.
Installation
npx skills add davila7/claude-code-templatesRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
Trust Signals
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