# DeepRefine: A New Method for Optimizing Agent Knowledge Bases via Reinforcement Learning

> DeepRefine proposes a reinforcement learning-based automatic knowledge base refinement framework that locates knowledge defects through multi-round interaction and abductive diagnosis, enabling incremental knowledge base optimization.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-11T12:48:31.000Z
- 最近活动: 2026-05-12T05:48:01.828Z
- 热度: 123.0
- 关键词: 知识库精炼, 强化学习, 大语言模型, 智能体, 溯因推理, GBD奖励, 知识库质量
- 页面链接: https://www.zingnex.cn/en/forum/thread/deeprefine-77e11667
- Canonical: https://www.zingnex.cn/forum/thread/deeprefine-77e11667
- Markdown 来源: floors_fallback

---

## [Introduction] DeepRefine: A Reinforcement Learning-Driven Automatic Refinement Framework for Agent Knowledge Bases

This article introduces DeepRefine—a reinforcement learning-based automatic refinement framework for agent knowledge bases. Addressing the three major defects of existing knowledge bases (incompleteness, inaccuracy, and redundancy), DeepRefine achieves incremental optimization through multi-round interactive exploration, abductive diagnosis for defect localization, and targeted refinement actions. Its innovative Gain-Beyond-Draft (GBD) reward mechanism solves the unsupervised training problem. Experiments show that this framework can significantly improve retrieval accuracy and downstream task performance, providing a new path for dynamic knowledge base optimization. Paper link: http://arxiv.org/abs/2605.10488v1

## Background: Three Major Quality Dilemmas of Agent Knowledge Bases

In LLM agent applications, external knowledge bases are crucial, but their expanding scale and frequent use lead to prominent quality issues:
1. **Incompleteness**: Missing key evidence or broken cross-document links;
2. **Inaccuracy**: Low-confidence or imprecise claims;
3. **Redundancy**: Ambiguous expressions and coreference resolution problems.
These defects accumulate to impair retrieval accuracy and downstream task performance.

## DeepRefine Core Process: Multi-Round Interaction → Abductive Diagnosis → Targeted Refinement

DeepRefine's workflow consists of three steps:
1. **Multi-round interactive knowledge exploration**: Continuously converse with the knowledge base and adjust exploration strategies based on previous results;
2. **Abductive diagnosis and defect localization**: Identify knowledge entries that need correction, supplementation, or deletion through advanced reasoning techniques that infer causes;
3. **Targeted refinement action execution**: Perform incremental update actions such as adding links, correcting claims, and eliminating redundancy in a targeted manner.

## Reinforcement Learning Innovation: GBD Reward Mechanism Solves Unsupervised Training Challenges

Since knowledge base refinement lacks standard reference answers, DeepRefine adopts the **Gain-Beyond-Draft (GBD)** reward mechanism:
- Core: Measure the difference in downstream task performance before and after refinement, and give positive rewards only when performance improves;
- Training: Use policy gradient methods to adjust reasoning strategies based on GBD signals and learn optimal interaction and refinement sequences.

## Experimental Validation: Significant Improvements of DeepRefine Across Multiple Tasks

Experiments validated the effectiveness across multiple knowledge-intensive tasks:
- **Retrieval accuracy**: After eliminating errors and redundancy, retrieval accuracy improved significantly;
- **Downstream tasks**: Performance in question answering, summary generation, reasoning chain construction, etc., outperformed the original knowledge base model;
- **Generalization ability**: Maintained stable performance improvements in cross-domain scenarios.

## Technical Significance and Prospects: From Static Construction to Dynamic Optimization

Significance of DeepRefine:
- Automated solution: Reduces reliance on manual review;
- Scalability: Facilitates integration of new actions and reward signals;
- Direction-setting: Promotes the shift from static construction to dynamic optimization of knowledge bases, and is expected to become a standard configuration for future agent knowledge bases.
Conclusion: DeepRefine combines reinforcement learning and abductive reasoning to open a new path for knowledge base quality optimization, and will support agents to provide better services in the future.