RAG with GRPO Fine-Tuned Reasoning Model

Group Relative Policy Optimization (GRPO) is an advanced technique in reinforcement learning applied to large language models to guide them towards desired outcomes without relying on ground truth labels. The process begins with pre-training a model on a vast dataset, followed by supervised fine-tuning (SFT) on specific data formats, such as instructions or question-answer pairs. In GRPO, multiple responses are generated for a prompt, and each response is scored based on predefined rules, with scores converted to a normalized z-score to determine which answers are accepted. Unlike traditional methods like PPO or DPO, GRPO does not use a critique or scorer model; instead, it employs multiple scoring functions to evaluate responses. The technique ensures model updates remain stable through a controlled clipping range and a scaling factor that manages KL divergence penalties, maintaining proximity to the reference model. GRPO also incorporates reward functions to enhance token diversity and control response length, and it is implemented using HuggingFace's tools, such as PEFT: LoRA, for efficient model training on limited hardware resources.