What is the main advantage of GRPO over PPO?

GRPO eliminates the need for a separate reward model by comparing multiple completions within a group, making it simpler to implement and more sample-efficient for alignment tasks.

Does this skill support memory-efficient training?

Yes, it includes configurations for 8-bit Adam optimization, LoRA, and Unsloth integration for training on consumer-grade hardware like single A100 or 3090/4090 GPUs.

Why does the loss increase during GRPO training?

Unlike SFT, GRPO loss measures KL divergence from the initial policy; an increasing loss is normal as the model diverges from its starting point to optimize for higher rewards.

Can I use this for simple text classification?

No, GRPO is best suited for tasks with clear, verifiable reward signals like math, coding, or strict format enforcement. Simple tasks are better handled via Supervised Fine-Tuning (SFT).

GRPO RL Fine-Tuning Expert

Name: GRPO RL Fine-Tuning Expert
Author: ovachiever

byovachiever

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Data Science & ML

Implements Group Relative Policy Optimization (GRPO) for training language models in reasoning, logic, and structured output tasks.

About

This skill provides comprehensive guidance and production-ready patterns for fine-tuning language models using GRPO and the TRL library. It specializes in teaching models to follow complex reasoning chains, enforce strict XML/JSON formats, and solve verifiable tasks like math or coding through reinforcement learning. By leveraging within-group comparisons rather than a separate reward model, it offers a more efficient path to aligning models with custom reward signals and domain-specific behaviors without the need for labeled preference data.

Key Features

5 GitHub stars
Advanced reward function design for format, correctness, and reasoning
Detailed troubleshooting for mode collapse and loss divergence
Memory-optimized configurations for single and multi-GPU setups
Standardized GRPO training workflows with TRL and Unsloth
Expert patterns for multi-stage reinforcement learning

Use Cases

Training specialized models for math and coding challenges with verifiable rewards
Enforcing strict output format compliance (JSON/Schema) without labeled preference data
Fine-tuning models to produce structured reasoning (Chain-of-Thought) within XML tags

About

Key Features

5 GitHub stars
Advanced reward function design for format, correctness, and reasoning
Detailed troubleshooting for mode collapse and loss divergence
Memory-optimized configurations for single and multi-GPU setups
Standardized GRPO training workflows with TRL and Unsloth
Expert patterns for multi-stage reinforcement learning

Use Cases

Training specialized models for math and coding challenges with verifiable rewards
Enforcing strict output format compliance (JSON/Schema) without labeled preference data
Fine-tuning models to produce structured reasoning (Chain-of-Thought) within XML tags