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npx versuz@latest install hiyenwong-ai-collection-collection-skills-beyond-stochastic-exploration-whatgit clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-beyond-stochastic-exploration-what/SKILL.md--- name: beyond-stochastic-exploration-what description: "Reinforcement learning (RL) has become an effective approach for advancing the reasoning capabilities of large language models (LLMs) through the strategic integration of external ... Activation: reinforcement, stochastic" --- # Beyond Stochastic Exploration: What Makes Training Data Valuable for Agentic Search ## Overview Reinforcement learning (RL) has become an effective approach for advancing the reasoning capabilities of large language models (LLMs) through the strategic integration of external search engines. However, current RL-based search agents often rely on a process of stochastic exploration guided by carefully crafted outcome rewards, leading to inefficient reasoning trajectories and unstable training. To address these issues, we propose a novel framework, Hierarchical Experience (HiExp), to enhance the performance and training stability of search agents. Specifically, we extract empirical knowledge through contrastive analysis and a multi-level clustering mechanism, transforming raw reasoning trajectories into hierarchical experience knowledge. By leveraging experience-aligned training, we effectively regularize stochastic exploration, evolving it into a strategic and experience-driven search process. Extensive evaluations on multiple complex agentic search and mathematical reasoning benchmarks demonstrate that our approach not only achieves substantial performance gains but also exhibits strong cross-task and cross-algorithm generalization. ## Source Paper - **Title**: Beyond Stochastic Exploration: What Makes Training Data Valuable for Agentic Search - **Authors**: Chuzhan Hao, Wenfeng Feng, Guochao Jiang, Guofeng Quan, Guohua Liu, Yuewei Zhang - **arXiv**: 2604.08124v1 - **Published**: 2026-04-09 - **Categories**: cs.AI - **Primary Category**: cs.AI ## Core Concepts This paper presents research on systems engineering with focus areas including: - Novel methodological frameworks - Theoretical foundations and analysis - Practical implementation strategies - Experimental validation ## Technical Contributions 1. **Novel Approach**: Advanced methodology for complex systems problems 2. **Theoretical Foundation**: Rigorous mathematical analysis 3. **Practical Implementation**: Real-world application and validation ## Applications - Systems engineering research and development - Distributed systems design and optimization - Control system implementation - Multi-agent coordination ## Implementation Guidelines 1. Review the source paper for detailed methodology 2. Understand the theoretical framework 3. Implement the proposed approach 4. Validate with appropriate experiments ## References - Chuzhan Hao et al. (2026). "Beyond Stochastic Exploration: What Makes Training Data Valuable for Agentic Search." arXiv:2604.08124v1. - arXiv URL: https://arxiv.org/abs/2604.08124v1 ## Activation Keywords reinforcement, stochastic