VEPO: Variable Entropy Policy Optimization Improves LLM Generation on Low-Resource Languages via Entropy-Adaptive RL Fine-Tuning

arXiv·low signal

Researchers published VEPO (Variable Entropy Policy Optimization), a reinforcement learning fine-tuning method targeting LLMs' chronic underperformance on low-resource languages caused by inefficient subword segmentation creating entropy imbalances during generation. VEPO dynamically adapts the entropy penalty coefficient during RL training based on language-specific token distribution, improving output quality without requiring separate per-language model variants. As AI agents are deployed globally across enterprise contexts, low-resource language optimization becomes an infrastructure-level gap — agents that fail on non-English inputs cannot operate in most of the world's markets.