arXiv:2509.24494v4 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) trains Chain-of-Thought reasoning with verifiable rewards, but estimating thought-level advantages without value functions often suffers from high variance. Although tree-style branching is used in practice to reduce variance, it lacks a theoretical explanation of why it works and whether it is important or potentially necessary. We study thought-level advantage estimation in GRPO from a variance perspective under a minimal tree-style setting where multiple continuations are sampled for each thought.
The continuous drive for more efficient and robust AI reasoning capabilities, particularly for complex tasks, necessitates ongoing research into foundational optimization techniques like those for Chain-of-Thought reasoning.
Improving the stability and efficiency of training methodologies for advanced AI reasoning directly impacts the reliability and feasibility of deploying more complex AI systems, such as AI agents, in real-world applications.
This research provides a theoretical understanding of why tree-style branching in GRPO reduces variance, potentially leading to more deliberate and optimized implementations of advanced AI reasoning frameworks.
- · AI researchers
- · Developers of AI agents
- · Sectors using complex AI for decision-making
- · Developers reliant on less optimized reasoning frameworks
More stable and efficient training for Chain-of-Thought AI models.
Accelerated development and adoption of AI agents capable of complex tasks with fewer errors.
Increased trust and reliance on autonomous AI systems for critical functions in various industries.
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Read at arXiv cs.CL