Tips for Effective RL with GRPO in Language Models

June 2025

Reinforcement learning (RL), especially in language model training using GRPO, is highly sensitive to how it's implemented. The default settings provided in common libraries are useful as a starting point but often need adjustment depending on your task and model. These defaults are based on both personal experiments and community input.

Start by evaluating your model and setup:

• If your model consistently fails to achieve any rewards after multiple attempts, the task might be too difficult. In that case, consider simplifying the task, warming up the model with supervised learning, or tweaking the prompts.
• If the model performs exceptionally well without any training, the task may be too easy. You might need to filter the dataset to increase its difficulty.

Strategies that might boost performance or speed (though they come with risks):

• Turning off the influence of a reference model completely
• Using a higher learning rate
• Increasing how many times the model updates for each batch of data

Approaches that can help make training more stable, but may reduce efficiency:

• Generating more responses per prompt
• Using larger batches of prompts during training
• Applying stronger gradient clipping
• Training on larger models (e.g., models with over 14 billion parameters)
• Expanding the size of response groups
• Using low-rank adaptation methods
• Filtering the dataset by difficulty level, though this requires extra upfront effort

Tactics that may work in some contexts but aren't universally effective:

• Using higher penalties for deviation from the reference model
• Comparing different versions of GRPO-based training approaches
• Aggressively filtering out overly long or irrelevant outputs
• Hiding certain parts of the environment or task responses during training

Some adjustments that often help without much downside:

• Gradually increasing the learning rate during the first few steps of training
• Regularly updating your reference model if you're using one, especially for longer training runs
• Overlapping inference and training steps for more efficient learning

To achieve effective learning, ensure there is variety in the reward scores for different responses to the same prompt. This helps the model learn to distinguish better responses from worse ones. (Refer to the DAPO paper for more insights on this.)

Task difficulty is key to success: Choose tasks that challenge your model appropriately—neither too simple nor overwhelmingly hard. This is the best way to promote useful diversity and robust learning.

For further insights, explore Hugging Face's open training logbooks, which include community findings, tips, and ongoing experiments.