Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models

• URL: http://arxiv.org/abs/2602.01970v1
• Date: Mon, 02 Feb 2026 11:24:36 GMT
• Title: Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models
• Authors: Yun Qu, Qi Wang, Yixiu Mao, Heming Zou, Yuhang Jiang, Weijie Liu, Clive Bai, Kai Yang, Yangkun Chen, Saiyong Yang, Xiangyang Ji,
• Abstract summary: This study introduces Generalizable Predictive Prompt Selection (GPS)<n>GPS performs Bayesian inference towards prompt difficulty using a lightweight generative model trained on the shared optimization history.<n> Experiments across varied reasoning benchmarks indicate GPS's substantial improvements in training efficiency, final performance, and test-time efficiency.
• Score: 46.50839982051244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning enhances the reasoning capabilities of large language models but often involves high computational costs due to rollout-intensive optimization. Online prompt selection presents a plausible solution by prioritizing informative prompts to improve training efficiency. However, current methods either depend on costly, exact evaluations or construct prompt-specific predictive models lacking generalization across prompts. This study introduces Generalizable Predictive Prompt Selection (GPS), which performs Bayesian inference towards prompt difficulty using a lightweight generative model trained on the shared optimization history. Intermediate-difficulty prioritization and history-anchored diversity are incorporated into the batch acquisition principle to select informative prompt batches. The small predictive model also generalizes at test-time for efficient computational allocation. Experiments across varied reasoning benchmarks indicate GPS's substantial improvements in training efficiency, final performance, and test-time efficiency over superior baseline methods.

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