research-grpo-rl-training
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
Source facts
- Repository
- GrayCodeAI/starling
- Last source activity
- July 13, 2026 at 09:28
- Detected SKILL.md language
- English
- Stars
- 2
- Forks
- 0
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Source instructions · Read-only preview- name
- research-grpo-rl-training
- description
- Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
- license
- MIT
- tags
- ["post-training","reinforcement-learning","grpo","trl","rlhf"]
- version
- 1.0.0
- author
- Orchestra Research
- dependencies
- ["transformers>=4.47.0","trl>=0.14.0","datasets>=3.2.0","peft>=0.14.0","torch"]
## Usage Instructions for Agents
When this skill is loaded:
1. **Read this entire file** before implementing GRPO training
2. **Start with the simplest reward function** (e.g., length-based) to validate setup
3. **Use the templates** in `templates/` directory as starting points
4. **Reference examples** in `examples/` for task-specific implementations
5. **Follow the workflow** sequentially (don't skip steps)
6. **Debug incrementally** - add one reward function at a time
**Critical Reminders:**
- Always use multiple reward functions (3-5 is optimal)
- Monitor reward metrics, not loss
- Test reward functions before training
- Start small (num_generations=4), scale up gradually
- Save checkpoints frequently (every 100 steps)
This skill is designed for **expert-level implementation**. Beginners should start with supervised fine-tuning before attempting GRPO.
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