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[2602.14169] Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling
Summary
The paper presents Deep Dense Exploration (DDE), a novel approach to enhance exploration in reinforcement learning for large language models, focusing on pivot-driven resampling to improve trajectory discovery.
Why It Matters
Effective exploration is crucial in reinforcement learning, especially for large language models. This research addresses significant limitations in existing methods, offering a new strategy that could lead to better performance in complex language tasks, thereby advancing the field of AI.
Key Takeaways
- DDE targets deep, recoverable states in unsuccessful trajectories to improve exploration.
- The method integrates a data-driven utility function to balance recoverability and depth bias.
- Local dense resampling at pivot states increases the likelihood of discovering correct trajectories.
- Experiments show DDE outperforms existing methods like GRPO and tree-based approaches.
- The dual-stream optimization objective separates global policy learning from local updates.
Computer Science > Machine Learning arXiv:2602.14169 (cs) [Submitted on 15 Feb 2026] Title:Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling Authors:Yiran Guo, Zhongjian Qiao, Yingqi Xie, Jie Liu, Dan Ye, Ruiqing Zhang, Shuang Qiu, Lijie Xu View a PDF of the paper titled Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling, by Yiran Guo and 7 other authors View PDF HTML (experimental) Abstract:Effective exploration is a key challenge in reinforcement learning for large language models: discovering high-quality trajectories within a limited sampling budget from the vast natural language sequence space. Existing methods face notable limitations: GRPO samples exclusively from the root, saturating high-probability trajectories while leaving deep, error-prone states under-explored. Tree-based methods blindly disperse budgets across trivial or unrecoverable states, causing sampling dilution that fails to uncover rare correct suffixes and destabilizes local baselines. To address this, we propose Deep Dense Exploration (DDE), a strategy that focuses exploration on $\textit{pivots}$-deep, recoverable states within unsuccessful trajectories. We instantiate DDE with DEEP-GRPO, which introduces three key innovations: (1) a lightweight data-driven utility function that automatically balances recoverability and depth bias to identify pivot states; (2) local dense resampling at each pivot to increase the probability of discover...
