Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO

Yiran Xu; Yiming Ren; Zicheng Lin; Chufan Shi; Yukang Chen; Dingdong Wang; Tianhe Wu; Junjie Wang; Yujiu Yang; Yu Qiao; Ruihang Chu

arXiv · 2605.30789

Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO

Yiran Xu·Yiming Ren·Zicheng Lin·Chufan Shi·Yukang Chen·Dingdong Wang·Tianhe Wu·Junjie Wang·Yujiu Yang·Yu Qiao·Ruihang Chu

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Abstract

We identify a new dimension for enhancing rollout diversity in Group Relative Policy Optimization (GRPO) for LLMs. While GRPO relies on diverse rollouts, prevailing strategies primarily increase diversity by injecting more token-level randomness, which may introduce step-wise noise and lead to incoherent trajectories. We uncover that smaller models within the same model family inherently exhibit higher policy-level diversity, indicated by their superior pass@k relative to larger counterparts as sample counts increase. Unlike token-level noise, this diversity is temporally correlated, preserves logical consistency, and provides structured exploration signals for gradient estimation. We thus propose S2L-PO (Small-to-Large Policy Optimization), a framework that leverages fixed small models as natural explorers to train larger models. To balance exploration and exploitation, we design a progressive annealing strategy that transitions from offline small-model rollouts to the large learner's own sampling. This shift elegantly avoids mid-training performance drops caused by the small model's capacity limits, achieving faster convergence and unlocking a higher performance ceiling. S2L-PO improves accuracy on diverse mathematical reasoning benchmarks (e.g., +8.8% on AIME 24 using a 1.7B explorer to guide the 8B model) while reducing rollout compute.

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BibTeX RIS

Yiran Xu, Yiming Ren, Zicheng Lin, Chufan Shi, Yukang Chen, Dingdong Wang, Tianhe Wu, Junjie Wang, Yujiu Yang, Yu Qiao, Ruihang Chu. 2026-07-24. Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO. https://arxiv.org/abs/2605.30789

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