Implement GRPO to Give LLMs Reasoning Ability with Qwen2.5‑0.5B

This article explains the GRPO reinforcement‑learning algorithm, shows its core idea of internal group competition without a separate evaluator model, and provides a complete, step‑by‑step code walkthrough—including environment setup, dataset preparation, reward‑function design, training configuration, and evaluation—using the Qwen2.5‑0.5B‑Instruct model on the GSM8K math dataset.

GRPOGSM8KQwen2.5

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Implement GRPO to Give LLMs Reasoning Ability with Qwen2.5‑0.5B

Network Intelligence Research Center (NIRC)

Apr 7, 2025 · Artificial Intelligence

Getting Started with Hugging Face TRL: Fine‑tune LLaVA using DPO

This guide introduces Hugging Face's TRL library, explains how to install it alongside Transformers, and walks through modifying LLaVA's trainer, dataset, and data collator to apply the DPO reinforcement‑learning algorithm for multimodal model fine‑tuning.

DPOHugging FaceLLaVA

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Getting Started with Hugging Face TRL: Fine‑tune LLaVA using DPO

Baobao Algorithm Notes

Mar 19, 2025 · Artificial Intelligence

Why Does GRPO Loss Start at Zero and Grow During OpenR1 Training?

The article explains why the GRPO loss in OpenR1 and trl starts at zero and then rises, detailing the underlying KL‑divergence formulation, the single‑step update mechanism, and how gradients are preserved despite a zero scalar loss, with code examples from the trl implementation.

GRPOLoss InitializationOpenR1

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Why Does GRPO Loss Start at Zero and Grow During OpenR1 Training?