# GRPO fine-tuning on Red Hat OpenShift AI: Reinforcement learning from verifiable rewards with Training Hub

[Red Hat](https://yomu.fyi/company/red-hat) · Fiona Waters · Aug 26, 2026

**Type:** Tutorial

## Summary

Supervised fine-tuning for structured reasoning tasks like tool calling often demands expensive, labor-intensive dataset curation. Group Relative Policy Optimization (GRPO) resolves this by using programmatic reward functions to score multiple candidate responses without requiring human labels or a dedicated reward model. On Red Hat OpenShift AI, developers can implement GRPO using Training Hub, Kubeflow Trainer, and the Kubeflow SDK to fine-tune models like Qwen3-4B. The setup utilizes vLLM for candidate generation alongside LoRA weight updates, sharing storage via ReadWriteMany persistent volume claims. In a practical tool-calling test, five training iterations raised task accuracy from 33% to 67%, teaching the model to emit parseable function invocations rather than fabricated conversational text.

## Context

Enterprise language model deployments often require structured reasoning, such as producing valid JSON, executable SQL, or accurate tool calls. Although supervised fine-tuning can improve these capabilities, creating and curating labelled training datasets is slow and expensive.

## Approach / What changed

The implementation runs a GRPO fine-tuning job on Red Hat OpenShift AI using Training Hub, Kubeflow Trainer, and the Kubeflow SDK. It fine-tunes Qwen3-4B on the Toucan-1.5M dataset with LoRA, splitting GPU memory between vLLM inference and training. Programmatic reward functions score candidate groups to update adapter weights over shared ReadWriteMany persistent storage.

## Takeaways

- GRPO eliminates the need for human labelling and separate reward models by using programmatic functions to score candidate responses against verifiable criteria.
- The OpenShift AI training setup requires ReadWriteMany persistent storage so both the JupyterLab workbench and the training pod can simultaneously access datasets, models, and checkpoints.
- Fine-tuning Qwen3-4B over five GRPO iterations improved tool-calling accuracy from 33% to 67%, shifting model behavior from fabricating conversational text to generating syntactically correct function calls.

**Tags:** [Kubernetes](https://yomu.fyi/topic/kubernetes), [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Python](https://yomu.fyi/topic/python)

[Read original post](https://developers.redhat.com/articles/2026/08/26/reinforcement-learning-from-verifiable-rewards-with-training-hub-on-red-hat-openshift-ai)