Group Relative Policy Optimization (GRPO) Algorithm

A Group Relative Policy Optimization (GRPO) Algorithm is a reinforcement learning algorithm that optimizes token-level agent policies through group-based relative reward comparisons for efficient training.

• AKA: GRPO Algorithm, Group-Based Policy Optimization, Relative Reward RL Algorithm, Token-Level Policy Optimization.
• Context:
  • It can typically compute GRPO Reward Signals through group-relative comparisons of action trajectorys.
  • It can typically update GRPO Policy Parameters using gradient estimation from relative performance metrics.
  • It can typically handle GRPO Token-Level Decisions in sequence generation tasks and navigation tasks.
  • It can typically improve GRPO Sample Efficiency compared to absolute reward methods.
  • It can often stabilize GRPO Training Processes through variance reduction.
  • It can often scale to Large Model Training with distributed computing.
  • It can often integrate with Transformer Architectures for language model training.
  • It can range from being a Small-Group GRPO Algorithm to being a Large-Group GRPO Algorithm, depending on its comparison group size.
  • It can range from being a Online GRPO Algorithm to being an Offline GRPO Algorithm, depending on its data collection strategy.
  • It can range from being a Single-Task GRPO Algorithm to being a Multi-Task GRPO Algorithm, depending on its training objective.
  • It can range from being a Discrete GRPO Algorithm to being a Continuous GRPO Algorithm, depending on its action space.
  • ...
• Example(s):
  • GRPO Implementations, such as:
    • WebSailor-V2-30B-A3B GRPO Training, for web navigation optimization.
    • Language Model GRPO, for text generation improvement.
    • Game Agent GRPO, for strategy learning.
  • GRPO Application Domains, such as:
    • Web Agent Training, optimizing navigation actions.
    • Dialogue System Training, improving response quality.
  • ...
• Counter-Example(s):
  • Proximal Policy Optimization (PPO), which uses absolute rewards.
  • Q-Learning Algorithm, which learns value functions.
  • Supervised Learning Algorithm, which requires labeled data.
• See: Reinforcement Learning Algorithm, Policy Gradient Method, Proximal Policy Optimization (PPO), WebSailor-V2-30B-A3B Model, Tongyi DeepResearch Agent, Token-Level Optimization, Relative Reward Learning, Agent Training Algorithm, Variance Reduction Technique, Deep Reinforcement Learning.

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