The bottom line: A CPU-based RL controller optimizes adaptive sampling during test-time scaling and reduces computational overhead and latency compared to heuristic methods.
Researchers have developed a lightweight sampling controller trained via reinforcement learning that dynamically decides during test-time scaling of language models when sufficient samples have been acquired. The method balances answer quality, latency, and computational cost and runs on CPUs.
Test-time scaling substantially improves the reasoning performance of large language models but introduces significant computational overhead and latency. Existing adaptive sampling methods such as ASC and ESC attempt to mitigate this through heuristic decision rules—but often rely on questionable distributional assumptions.
The new work (arXiv:2606.03102) formulates adaptive sampling as a Markov decision process (MDP). A lightweight RL-trained controller makes the decision at each round: acquire further samples or stop sampling. The controller uses only statistics from final answers and requires CPU resources during both training and deployment—no GPU.
Theoretically, the authors interpret the system as a Lagrange relaxation of a constrained optimization problem with explicit budget constraints. Experimental comparisons against ASC and ESC demonstrate improved trade-offs between answer accuracy, sampling rounds, and total number of required samples.
For engineers, the method is relevant because it reduces inference costs and latency in reasoning tasks without requiring specialized hardware. The MDP formulation also allows direct extensions, such as for alternative cost functions or multi-token scenarios.
Source: arxiv.org · Published June 2, 2026
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