arXiv:2509.21154v4 Announce Type: replace Abstract: Process reward models (PRMs) allow for fine-grained credit assignment in reinforcement learning (RL), and seemingly contrast with outcome reward models (ORMs), which assign a single reward to an entire trajectory. However, we provide theoretical proof in this work that the Group Relative Policy Optimization (GRPO) RL algorithm equipped with an ORM is in fact equivalent to a PRM-aware RL objective equipped with a non-trivial, Monte-Carlo-based PRM (given mild assumptions). Leveraging the framework of GRPO-as-a-PRM, we identify a flaw in the GR
The continuous academic research in AI, particularly in reinforcement learning, regularly uncovers deeper theoretical connections between seemingly distinct methodologies, driven by the push for more efficient and robust AI systems.
Understanding the equivalence between different reward modeling approaches can lead to more robust and generalizable reinforcement learning algorithms, directly improving the capabilities of AI agents and autonomous systems.
This theoretical work suggests that certain existing algorithms (like GRPO with ORMs) are implicitly achieving the benefits of process reward models, potentially streamlining future AI development and leading to more efficient fine-grained credit assignment.
- · AI researchers
- · Reinforcement learning developers
- · AI agents
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The theoretical unification of GRPO and process reward models simplifies parts of reinforcement learning theory and practice.
This improved theoretical understanding could accelerate the development of more sophisticated and capable AI agents, particularly in complex, multi-step tasks.
More efficient and powerful AI agents could expand their application across various industries, impacting automation and potentially collapsing white-collar workflows faster than anticipated.
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Read at arXiv cs.LG