Group Relative Policy Optimization (GRPO): How Eliminating Value Models Scaled LLM Reasoning
Post-training reinforcement learning (RL) has become the primary mechanism for scaling reasoning capabilities in large language models. While early reinforcement learning from human feedback (RLHF) focused on conversational style and safety alignment, extending RL to multi-step reasoning domains such as mathematics, algorithmic coding, and formal logic exposed critical limitations in classical algorithms. Standard Proximal Policy Optimization (PPO), long the foundational algorithm for instructi



