EAGER: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling
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 training-free generation
method that leverages model uncertainty through token-wise entropy distribution
to reduce redundant computation and concurrently improve overall performance.
EAGer allows branching to multiple reasoning paths only in the presence of
high-entropy tokens, and then reallocates the saved compute budget to the
instances where exploration of alternative paths is most needed. We find that
across multiple open-source models on complex reasoning benchmarks such as AIME
2025, EAGer can reallocate the budget without accessing target labels,
achieving the best efficiency-performance trade-off in terms of reasoning
length and Pass@k. When target labels are accessible, EAGer generates up to 65%
fewer tokens (hence saving compute) and achieves up to 37% improvement in
Pass@k compared to the Full Parallel Sampling.
68f0d8cc36f8b025381e1b5a
Daniel Scalena
claimed_verified
2025-10-17T04:12:56.509Z
633e9ae8630ed415eb38073c
https://cdn-avatars.huggingface.co/v1/production/uploads/633e9ae8630ed415eb38073c/FtSb_ybm08qBR1liISKQ0.jpeg
Daniel Scalena
user
DanielSc4
68f0d8cc36f8b025381e1b5b
Leonidas Zotos
68f0d8cc36f8b025381e1b5c
Elisabetta Fersini
68f0d8cc36f8b025381e1b5d
Malvina Nissim
68f0d8cc36f8b025381e1b5e
Ahmet Üstün
EAGER: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling
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 training-free generation
method that leverages model uncertainty through token-wise entropy distribution
to reduce redundant computation and concurrently improve overall performance.
EAGer allows branching to multiple reasoning paths only in the presence of
high-entropy tokens, and then reallocates the saved compute budget to the
instances where exploration of alternative paths is most needed. We find that
across multiple open-source models on complex reasoning benchmarks such as AIME
2025, EAGer can reallocate the budget without accessing target labels,
achieving the best efficiency-performance trade-off in terms of reasoning
length and Pass@k. When target labels are accessible, EAGer generates up to 65%
fewer tokens (hence saving compute) and achieves up to 37% improvement in
Pass@k compared to the Full Parallel Sampling.
EAGer, a training-free method, uses token-wise entropy to optimize computational resources and improve performance on complex reasoning tasks.
reasoning language models test-time scaling model uncertainty token-wise entropy high-entropy tokens reasoning length Pass@k Full Parallel Sampling