mlsanityData to 14 Oct 2026

elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settings

arXiv 2507.08705 ↗First posted 11 Jul 2025cs.AIcited by 0

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

Abstract (arXiv)

We present elsciRL, an open-source Python library to facilitate the application of language solutions on reinforcement learning problems. We demonstrate the potential of our software by extending the Language Adapter with Self-Completing Instruction framework defined in (Osborne, 2024) with the use of LLMs. Our approach can be re-applied to new applications with minimal setup requirements. We provide a novel GUI that allows a user to provide text input for an LLM to generate instructions which it can then self-complete. Empirical results indicate that these instructions textitcan improve a reinforcement learning agent's performance. Therefore, we present this work to accelerate the evaluation of language solutions on reward based environments to enable new opportunities for scientific discovery.

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 10 of its 18 references that are in the map. First posted on arXiv 11 Jul 2025.

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10 of 18 references.

References · 10

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,463
  5. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
  6. 2016A Deep Hierarchical Approach to Lifelong Learning in Minecraftcited by 90
  7. 2017Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learningcited by 77
  8. 2020MushroomRL: Simplifying Reinforcement Learning Researchcited by 50
  9. 2019Hierarchical Decision Making by Generating and Following Natural Language Instructionscited by 30
  10. 2021A Survey of Text Games for Reinforcement Learning informed by Natural Languagecited by 14

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  1. 2025Prompted Policy Search: Reinforcement Learning through Linguistic and Numerical Reasoning in LLMs
  2. 2024Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods
  3. 2024Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search
  4. 2025Boosting Universal LLM Reward Design through Heuristic Reward Observation Space Evolution
  5. 2023Reward Design with Language Models
  6. 2025Tool-R1: Sample-Efficient Reinforcement Learning for Agentic Tool Use
  7. 2024Words as Beacons: Guiding RL Agents with High-Level Language Prompts
  8. 2025LANPO: Bootstrapping Language and Numerical Feedback for Reinforcement Learning in LLMs