grpo-rl-training

Implement GRPO training with TRL by preparing a dataset, designing custom reward functions for within-group comparisons, configuring training hyperparameters, and optionally applying PEFT via LoRA for production-grade RLHF.

Group Relative Policy Optimization is used for expert-level alignment tasks in NLP, including enforcing output structure, improving reasoning, and balancing format and correctness across domains through within-group reward comparisons.

Design custom reward functions for GRPO in TRL by creating templates that evaluate within-group comparisons, targeting multi-objective goals like reasoning quality, output formatting, and task correctness across domains.

Yes, you can use LoRA and PEFT for GRPO training in TRL. The skill provides optional PEFT via LoRA integration, requiring peft>=0.14.0, alongside transformers>=4.47.0 and trl>=0.14.0.

The best way to start a GRPO training workflow is to read the skill documentation, copy the basic training template, and adapt the dataset preparation, reward functions, and training config for your specific reasoning task.

Dependencies required for GRPO training with TRL include transformers>=4.47.0, trl>=0.14.0, datasets>=3.2.0, peft>=0.14.0, and torch to execute production-grade reinforcement learning workflows.