Kinetics: Rethinking Test-Time Scaling Laws
Authors: name: Ranajoy Sadhukhan, name: Zhuoming Chen, name: Haizhong Zheng, name: Yang Zhou, name: Emma Strubell, name: Beidi Chen

We rethink test-time scaling laws from a practical efficiency perspective,
revealing that the effectiveness of smaller models is significantly
overestimated. Prior work, grounded in compute-optimality, overlooks critical
memory access bottlenecks introduced by inference-time strategies (e.g.,
Best-of-N, long CoTs). Our holistic analysis, spanning models from 0.6B to
32B parameters, reveals a new Kinetics Scaling Law that better guides resource
allocation by incorporating both computation and memory access costs. Kinetics
Scaling Law suggests that test-time compute is more effective when used on
models above a threshold than smaller ones. A key reason is that in TTS,
attention, rather than parameter count, emerges as the dominant cost factor.
Motivated by this, we propose a new scaling paradigm centered on sparse
attention, which lowers per-token cost and enables longer generations and more
parallel samples within the same resource budget. Empirically, we show that
sparse attention models consistently outperform dense counterparts, achieving
over 60 points gains in low-cost regimes and over 5 points gains in high-cost
regimes for problem-solving accuracy on AIME, encompassing evaluations on
state-of-the-art MoEs. These results suggest that sparse attention is essential
for realizing the full potential of test-time scaling because, unlike training,
where parameter scaling saturates, test-time accuracy continues to improve
through increased generation. The code is available at
https://github.com/Infini-AI-Lab/Kinetics.