mlsanityData to 14 Oct 2026

GLIDE-RL: Grounded Language Instruction through DEmonstration in RL

arXiv 2401.02991 ↗First posted 3 Jan 2024cs.CL cs.AI cs.LGcited by 2

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

Abstract (arXiv)

One of the final frontiers in the development of complex human - AI collaborative systems is the ability of AI agents to comprehend the natural language and perform tasks accordingly. However, training efficient Reinforcement Learning (RL) agents grounded in natural language has been a long-standing challenge due to the complexity and ambiguity of the language and sparsity of the rewards, among other factors. Several advances in reinforcement learning, curriculum learning, continual learning, language models have independently contributed to effective training of grounded agents in various environments. Leveraging these developments, we present a novel algorithm, Grounded Language Instruction through DEmonstration in RL (GLIDE-RL) that introduces a teacher-instructor-student curriculum learning framework for training an RL agent capable of following natural language instructions that can generalize to previously unseen language instructions. In this multi-agent framework, the teacher and the student agents learn simultaneously based on the student's current skill level. We further demonstrate the necessity for training the student agent with not just one, but multiple teacher agents. Experiments on a complex sparse reward environment validates the effectiveness of our proposed approach.

Key facts. Cited by 2 papers in the map (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 3 Jan 2024.

Builds on

21 of 34 references.

References · 21

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2017Hindsight Experience Replaycited by 1,136
  3. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  4. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 373
  5. 2018Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigationcited by 305
  6. 2017Automatic Goal Generation for Reinforcement Learning Agentscited by 259
  7. 2023Guiding Pretraining in Reinforcement Learning with Large Language Modelscited by 187
  8. 2022Goal-Conditioned Reinforcement Learning: Problems and Solutionscited by 158
  9. 2019A Survey of Reinforcement Learning Informed by Natural Languagecited by 154
  10. 2017Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Playcited by 141
  11. 2019Using Natural Language for Reward Shaping in Reinforcement Learningcited by 114
  12. 2017Grounded Language Learning in a Simulated 3D Worldcited by 100
9 more
  1. 2017Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learningcited by 98
  2. 2020Learning with AMIGo: Adversarially Motivated Intrinsic Goalscited by 88
  3. 2020Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Surveycited by 85
  4. 2018Learning to Understand Goal Specifications by Modelling Rewardcited by 80
  5. 2017Gated-Attention Architectures for Task-Oriented Language Groundingcited by 72
  6. 2021Asymmetric self-play for automatic goal discovery in robotic manipulationcited by 72
  7. 2019ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learningcited by 17
  8. 2022It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generationcited by 14
  9. 2019ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrationscited by 5

Built on by

Cited by year (2026 to 14 Oct)

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

2 papers.

  1. 2026SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learningcited by 1
  2. 2024A Research Agenda for Usability and Generalisation in Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Words as Beacons: Guiding RL Agents with High-Level Language Prompts
  2. 2024Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use
  3. 2024Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication
  4. 2025Ground-Compose-Reinforce: Grounding Language in Agentic Behaviours using Limited Data
  5. 2025Meta-RL Induces Exploration in Language Agents
  6. 2024TeaMs-RL: Teaching LLMs to Generate Better Instruction Datasets via Reinforcement Learning
  7. 2024Natural Language Reinforcement Learning
  8. 2024Game On: Towards Language Models as RL Experimenters