mlsanityData to 14 Oct 2026

GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning

arXiv 2411.14479 ↗First posted 19 Nov 2024cs.CL cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (arXiv)

Large language models (LLMs) have demonstrated impressive success in a wide range of natural language processing (NLP) tasks due to their extensive general knowledge of the world. Recent works discovered that the performance of LLMs is heavily dependent on the input prompt. However, prompt engineering is usually done manually in a trial-and-error fashion, which can be labor-intensive and challenging in order to find the optimal prompts. To address these problems and unleash the utmost potential of LLMs, we propose a novel LLMs-agnostic framework for prompt optimization, namely GRL-Prompt, which aims to automatically construct optimal prompts via reinforcement learning (RL) in an end-to-end manner. To provide structured action/state representation for optimizing prompts, we construct a knowledge graph (KG) that better encodes the correlation between the user query and candidate in-context examples. Furthermore, a policy network is formulated to generate the optimal action by selecting a set of in-context examples in a rewardable order to construct the prompt. Additionally, the embedding-based reward shaping is utilized to stabilize the RL training process. The experimental results show that GRL-Prompt outperforms recent state-of-the-art methods, achieving an average increase of 0.10 in ROUGE-1, 0.07 in ROUGE-2, 0.07 in ROUGE-L, and 0.05 in BLEU.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 21 of its 34 references that are in the map. First posted on arXiv 19 Nov 2024.

Builds on

21 of 34 references.

References · 21

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  5. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  6. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
  7. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
  8. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,135
  9. 2022A Survey on In-context Learningcited by 721
  10. 2020Heterogeneous Graph Transformercited by 582
  11. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 366
  12. 2022PromptSource: An Integrated Development Environment and Repository for Natural Language Promptscited by 289
9 more
  1. 2023Compositional Exemplars for In-context Learningcited by 144
  2. 2023Unified Demonstration Retriever for In-Context Learningcited by 142
  3. 2023Boosting Language Models Reasoning with Chain-of-Knowledge Promptingcited by 77
  4. 2024In-context Learning with Retrieved Demonstrations for Language Models: A Surveycited by 70
  5. 2022Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibrationcited by 24
  6. 2024An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaborationcited by 16
  7. 2021STaCK: Sentence Ordering with Temporal Commonsense Knowledgecited by 7
  8. 2024Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extractioncited by 7
  9. 2023PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matchingcited by 6

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  1. 2025PRL: Prompts from Reinforcement Learning
  2. 2025Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning
  3. 2026TAPR: Enhancing LLM Performance with a Task-Aware Prompt Rewriter
  4. 2024Reinforcement Learning Problem Solving with Large Language Models
  5. 2026Prompting Policies for Multi-step Reasoning and Tool-Use in Black-box LLMs with Iterative Distillation of Experience
  6. 2025PARL: Prompt-based Agents for Reinforcement Learning
  7. 2024StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language Models
  8. 2024Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods