Generalizing Test-time Compute-optimal Scaling as an Optimizable Graph
Authors: name: Fali Wang, name: Jihai Chen, name: Shuhua Yang, name: Runxue Bao, name: Tianxiang Zhao, name: Zhiwei Zhang, name: Xianfeng Tang, name: Hui Liu, name: Qi He, name: Suhang Wang
Organization: Pennsylvania State University

Test-Time Scaling (TTS) improves large language models (LLMs) by allocating
additional computation during inference, typically through parallel,
sequential, or hybrid scaling. However, prior studies often assume fixed
collaboration architectures (e.g., topologies) and single-model usage,
overlooking that optimal architectures and model combinations can vary across
tasks. Therefore, we study the novel problem of searching for compute-optimal
model combinations and architectures in TTS under a fixed budget. We formalize
it as a multi-LLM collaboration graph, where nodes encode roles and LLM model
assignments, and edges capture information flow. This problem is challenging
because (i) the combinatorial search space is prohibitively large, and (ii)
task-specific requirements demand tailored designs. To address these, we
reformulate the problem as probabilistic graph optimization and, through pilot
experiments, derive three empirical insights into TTS collaboration graphs.
Guided by these insights, we propose Agent-REINFORCE, an LLM-agent-augmented
framework that mirrors the REINFORCE pipeline by mapping
sampling-gradient-update to sampling-feedback-update, where feedback serves as
a textual gradient to update the probabilistic graph and efficiently search for
optimal multi-LLM collaboration graphs. Experiments show that Agent-REINFORCE
outperforms both traditional and LLM-based baselines in sample efficiency and
search performance, and effectively identifies optimal graphs under joint
objectives of accuracy and inference latency.