arXiv cs.AI· Bo Yang, Zishuai Zhang, Weihua Li, Qinnan Zhang, Sijia Wen, Hainan Zhang·· 13 小时前AI 评分31
Guided-RLVR：用引导式探索增强带可验证奖励的强化学习
Guided-RLVR: Guided Exploration in Reinforcement Learning with Verifiable Rewards
AI 导读
Guided-RLVR 在 RLVR 中引入轻量控制器，向采样分布注入语义引导 token，让模型从多种推理视角解题以扩大探索空间。在数学推理与代码生成基准上，该方法一致优于现有 RLVR 方法，并产生更多样的推理轨迹。
正文 · 原文
Abstract:Reinforcement learning with verifiable rewards (RLVR) has shown strong empirical gains in improving the reasoning ability of large language models (LLMs) on tasks such as mathematics and programming. However, recent studies have shown that RLVR rollouts often collapse onto a narrow set of reasoning trajectories, limiting the explored reasoning space. Motivated by findings that thinking tokens at critical steps serve as critical structural elements in LLM reasoning pathways, we propose Guided-RLVR, a novel approach that augments RLVR with a lightweight controller. The controller strategically injects semantically meaningful steering tokens into the sampling distribution, encouraging the model to approach problems from multiple reasoning perspectives and thereby expanding the exploration space. Experiments on mathematical reasoning and code generation benchmarks show that Guided-RLVR consistently outperforms existing RLVR methods while producing more diverse reasoning trajectories.
| Comments: | 14 pages, 7 figures, 8 tables |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.37041 [cs.AI] |
| (or arXiv:2610.37041v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37041 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bo Yang [view email]
[v1]
Tue, 27 Oct 2026 11:13:13 UTC (1,928 KB)
来源：arXiv cs.AI · arxiv.org