Group Relative Policy Optimization (GRPO), pioneered by DeepSeek, improves model alignment by using group-based preference comparisons instead of scalar reward models. It’s a step forward from traditional RLHF, offering more nuanced human feedback and better output quality.
But what comes after GRPO?
This topic explores emerging and theoretical methods poised to redefine LLM alignment:
- Self-Reflective Reinforcement Learning: Models that reason about their own uncertainty, revise answers, and minimize hallucinations.
- Tool-Augmented Alignment Loops: Teaching models to use search, calculators, and code execution as part of their learning.
- Multi-Agent Alignment Systems: Using cooperative or adversarial AI agents to help models critique and improve one another.
- Auto-Evolving Reward Models: Systems where reward functions evolve through simulated environments or human-in-the-loop debate.
We’ll also discuss the risks and challenges of scaling these methods toward safe, general-purpose intelligence—where models align not just with individual prompts, but with long-term goals, values, and reasoning principles.