A First-Principles Derivation of LLM Policy Optimization: From Expected Reward to GRPO and Its Structural Extensions
arXiv:2606.16733v1 Announce Type: new Abstract: Policy gradient algorithms for language models optimize the same objective $J(\theta) = \mathbb{E}*{\tau \sim p*\theta(\tau)}[R(\tau)]$, which has exactly two factors: the trajectory probability $p_\theta(\tau)$ and the reward $R(\tau)$. Every method from REINFORCE to PPO to GRPO and their descendants modifies one or both factors to address a specific failure in the preceding formulation. Existing surveys organize these methods by domain or chronology, which obscures the rationale behind each design choice and the precise location of its interven
The paper provides a foundational, first-principles derivation of LLM policy optimization techniques, which is crucial for advancing AI agent capabilities as LLMs become more complex and integrated into autonomous systems.
A deeper theoretical understanding of LLM policy optimization enables more effective and efficient development of advanced AI agents, leading to breakthroughs in diverse applications and potentially faster progress in artificial general intelligence.
This theoretical work provides a unifying framework for understanding various policy gradient algorithms, which will likely lead to more robust and powerful methods for training large language models and other AI systems.
- · AI researchers
- · AI development companies
- · Reinforcement learning practitioners
- · Those relying solely on empirical trial-and-error in AI optimization
Improved efficiency and performance of AI training methodologies, especially for complex tasks.
Faster development and deployment of more capable autonomous AI agents in various industries.
Accelerated progress towards AGI and new paradigms for human-computer interaction based on highly optimized LLM agents.
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Read at arXiv cs.AI