mlsanityData to 7 Oct 2026

Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook

arXiv 2210.13623 ↗First posted 24 Oct 2022cs.AI cs.CL cs.LGcited by 10

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Abstract (arXiv)

In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing. While most speech and language applications of reinforcement learning algorithms are centered around improving the training of deep neural networks with its flexible optimization properties, there are still many grounds to explore to utilize the benefits of reinforcement learning, such as its reward-driven adaptability, state representations, temporal structures and generalizability. In this survey, we present an overview of recent advancements of reinforcement learning and bandits, and discuss how they can be effectively employed to solve speech and natural language processing problems with models that are adaptive, interactive and scalable.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 86 of its 184 references that are in the map. First posted on arXiv 24 Oct 2022.

Builds on

86 of 184 references.

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50 most cited of 86 All 86 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 4; 2025: 0; 2026: 1.

10 papers.

  1. 2023From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscapecited by 18
  2. 2023Towards Healthy AI: Large Language Models Need Therapists Toocited by 13
  3. 2023Reinforcement Learning for Generative AI: A Surveycited by 11
  4. 2023Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamicscited by 6
  5. 2024Enhancing Trust in Autonomous Agents: An Architecture for Accountability and Explainability through Blockchain and Large Language Modelscited by 2
  6. 2023A Reinforcement Learning Framework for Online Speaker Diarizationcited by 1
  7. 2026A Component-Based Survey of Interactions between Large Language Models and Multi-Armed Banditscited by 1
  8. 2024Towards Evaluating Large Language Models for Graph Query Generationcited by 0
  9. 2024Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPscited by 0
  10. 2024Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learningcited by 0