EAGer: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling

ArXi:2510.11170v2 Announce Type: replace-cross With the rise of reasoning language models and test-time scaling methods as a paradigm for improving model performance, substantial computation is often required to generate multiple candidate sequences from the same prompt. This enables exploration of different reasoning paths toward the correct solution, however, allocates the same compute budget for each prompt. Grounded on the assumption that different prompts carry different degrees of complexity, and thus different computation needs, we propose EAGer, a.