Does this skill support parameter-efficient training like LoRA?

Yes, the skill includes production-ready implementation patterns for both full fine-tuning and parameter-efficient fine-tuning (PEFT) using LoRA and Unsloth.

What is the primary advantage of GRPO over PPO?

GRPO is more sample-efficient and significantly simpler to implement because it eliminates the need for a separate reward model, instead comparing multiple completions within a group.

Why is my training loss increasing during GRPO training?

In GRPO, an increasing loss is often normal as it measures the KL divergence from the initial policy; you should prioritize monitoring reward metrics and reward standard deviation to gauge progress.

Can I use this skill for standard supervised fine-tuning?

No, GRPO is specifically designed for Reinforcement Learning with reward signals. For basic text-to-text mapping tasks, standard Supervised Fine-Tuning (SFT) is more appropriate.

GRPO RL Fine-Tuning

Name: GRPO RL Fine-Tuning
Author: zechenzhangAGI

byzechenzhangAGI

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384

Data Science & ML

Implements Group Relative Policy Optimization (GRPO) using the TRL library to enhance model reasoning and structured output capabilities.

About

This skill provides comprehensive, expert-level guidance for training language models through Reinforcement Learning (RL) using the efficient GRPO algorithm. It allows developers to align models with complex domain behaviors—such as multi-step reasoning, strict XML/JSON formatting, and verifiable task accuracy—without the need for expensive labeled preference data. By leveraging the Transformer Reinforcement Learning (TRL) library, this skill offers battle-tested patterns for reward function design, memory-optimized training configurations, and performance-boosting integrations like Unsloth and vLLM.

Key Features

Full GRPO algorithm implementation for reasoning-heavy tasks
Standard and Unsloth-optimized workflow patterns
Memory-efficient training configurations for various GPU scales
384 GitHub stars
Template library for multi-objective reward functions (Correctness, Format, Style)
Diagnostic guidance for monitoring reward stability and KL divergence

Use Cases

Optimizing model behavior for multiple simultaneous objectives using custom reward signals
Enforcing strict adherence to domain-specific structured formats like JSON or custom XML tags
Teaching models to solve complex mathematical or coding problems with verifiable reasoning chains

About

Key Features

Full GRPO algorithm implementation for reasoning-heavy tasks
Standard and Unsloth-optimized workflow patterns
Memory-efficient training configurations for various GPU scales
384 GitHub stars
Template library for multi-objective reward functions (Correctness, Format, Style)
Diagnostic guidance for monitoring reward stability and KL divergence

Use Cases

Optimizing model behavior for multiple simultaneous objectives using custom reward signals
Enforcing strict adherence to domain-specific structured formats like JSON or custom XML tags
Teaching models to solve complex mathematical or coding problems with verifiable reasoning chains