Technical Architecture and Efficiency - RLHF (Reinforcement Learning from Human Feedback) requires 4 live models running simultaneously, leading to high computational costs and training instability at production scale [2][6] - DPO (Direct Preference Optimization) eliminates the need for an explicit reward model and critic by deriving reward signals implicitly from preference pairs, significantly reducing pipeline complexity [3] - GRPO (Group Relative Policy Optimization), introduced by DeepSeek in 2024, removes the critic model by utilizing group statistics (mean and standard deviation) as a baseline, thereby lowering overhead compared to PPO [4][5][7] Alignment Methodology and Trade-offs - RLHF relies on external scores and an internal critic to measure advantage, necessitating a pre-trained reward model based on human preferences [2][7] - DPO requires high-quality labeled preference pairs upfront and lacks online exploration, which may result in model brittleness if failure modes are not adequately covered in the data [3][4] - GRPO maintains the RL loop while generating its own baseline from a group of outputs, making it highly effective for verifiable tasks such as mathematics and coding [4][7][8] Future Industry Trends - The industry is shifting toward RULER (LLM-as-judge) frameworks to extend RL training to open-ended agentic tasks where programmatic verification is unavailable [8] - Model alignment strategies are evolving to minimize the "machinery" required, moving from complex multi-model setups toward more streamlined, self-referential optimization techniques [7][8]