AIBullisharXiv – CS AI · 3h ago7/10
🧠
EAGer: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling
Researchers introduce EAGer, a training-free method that optimizes inference-time computation for reasoning language models by dynamically allocating compute budgets based on token-level entropy. The approach reduces computational waste while improving performance, achieving up to 37% gains in Pass@k metrics with 59% fewer tokens in supervised settings.