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    <abstract>Reinforcement Learning with Verifiable Rewards (RLVR) has markedly enhanced the reasoning abilities of large language models (LLMs). Its success, however, largely depends on strong base models with rich world knowledge, yielding only modest improvements for small-size language models (SLMs). To address this limitation, we investigate Guided GRPO, which injects ground-truth reasoning steps into roll-out trajectories to compensate for SLMs’ inherent weaknesses. Through a comprehensive study of various guidance configurations, we find that naively adding guidance delivers limited gains. These insights motivate G²RPO-A, an adaptive algorithm that automatically adjusts guidance strength in response to the model’s evolving training dynamics. Experiments on mathematical reasoning and code-generation benchmarks confirm that G²RPO-A substantially outperforms vanilla GRPO. Our code and models at available at https://github.com/T-Lab-CUHKSZ/G2RPO-A.</abstract>
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%0 Conference Proceedings
%T G²RPO-A: Guided Group Relative Policy Optimization with Adaptive Guidance
%A Guo, Yongxin
%A Deng, Wenbo
%A Cheng, Zhenglin
%A Tang, Xiaoying
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F guo-etal-2026-g2rpo
%X Reinforcement Learning with Verifiable Rewards (RLVR) has markedly enhanced the reasoning abilities of large language models (LLMs). Its success, however, largely depends on strong base models with rich world knowledge, yielding only modest improvements for small-size language models (SLMs). To address this limitation, we investigate Guided GRPO, which injects ground-truth reasoning steps into roll-out trajectories to compensate for SLMs’ inherent weaknesses. Through a comprehensive study of various guidance configurations, we find that naively adding guidance delivers limited gains. These insights motivate G²RPO-A, an adaptive algorithm that automatically adjusts guidance strength in response to the model’s evolving training dynamics. Experiments on mathematical reasoning and code-generation benchmarks confirm that G²RPO-A substantially outperforms vanilla GRPO. Our code and models at available at https://github.com/T-Lab-CUHKSZ/G2RPO-A.
%U https://aclanthology.org/2026.acl-long.206/
%P 4525-4539

Markdown (Informal)

[G2RPO-A: Guided Group Relative Policy Optimization with Adaptive Guidance](https://aclanthology.org/2026.acl-long.206/) (Guo et al., ACL 2026)

• G2RPO-A: Guided Group Relative Policy Optimization with Adaptive Guidance (Guo et al., ACL 2026)

ACL

• Yongxin Guo, Wenbo Deng, Zhenglin Cheng, and Xiaoying Tang. 2026. G2RPO-A: Guided Group Relative Policy Optimization with Adaptive Guidance. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4525–4539, San Diego, California, United States. Association for Computational Linguistics.