Beyond Normalization: Rethinking the Partition Function as a Difficulty Scheduler for RLVR

Researchers in the field of artificial intelligence and machine learning have introduced a novel approach to addressing the trade-off between reasoning performance and output diversity in language models, reinterpreting the partition function as a difficulty scheduler for Reinforcement Learning for Vision and Reasoning (RLVR) in a paper released on February 26, 2026, aiming to solve the persistent problem where reward-maximizing reinforcement learning methods enhance LLM reasoning capabilities but often reduce the diversity of generated outputs. The research challenges conventional approaches that utilize GFlowNets to train language models to match target distributions while treating the partition function merely as a mathematical normalizer. Instead, the authors propose viewing this partition function as a per-prompt expected-reward signal that can dynamically adjust learning difficulty based on individual prompt complexity. This innovative perspective allows for maintaining high reasoning performance across various tasks while preserving the essential diversity of outputs that makes language models more versatile and capable in real-world applications. The methodology represents a significant advancement in the field of reinforcement learning for language models, potentially addressing one of the most persistent challenges in developing increasingly capable AI systems.