Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One | Paper Citation Record

Observation 7ad5cac9-df3a-44a8-bfda-9bd22a7c4f8b · outbound

This paper cites Reinforcement learning: An introduction.A Bradford Book, 2018.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Reinforcement learning: An introduction.A Bradford Book, 2018

Reference 1

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Observation b65d7d72-dd7a-4714-becf-e4a3fe7b93d8 · outbound

This paper cites An introduction to deep reinforcement learning.Foundations and Trends® in Machine Learning, 11(3-4):219–354, 2018.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One An introduction to deep reinforcement learning.Foundations and Trends® in Machine Learning, 11(3-4):219–354, 2018

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.660069Z

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Observation e379d43f-4d11-4265-8313-801cd0839fad · outbound

This paper cites Mastering the game of go without human knowledge.nature, 550(7676):354–359, 2017.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Mastering the game of go without human knowledge.nature, 550(7676):354–359, 2017

Reference 3

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This paper cites Grandmaster level in starcraft ii using multi-agent reinforcement learning.nature, 575(7782):350–354, 2019.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Grandmaster level in starcraft ii using multi-agent reinforcement learning.nature, 575(7782):350–354, 2019

Reference 4

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Observation b5c85c06-0d0d-4240-a7f2-d61777df09e3 · outbound

This paper cites Dota 2 with Large Scale Deep Reinforcement Learning.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Dota 2 with Large Scale Deep Reinforcement Learning

Reference 5

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Observation eb94c72c-b50b-45d6-98d8-4ab6433b0bf5 · outbound

This paper cites Mastering atari games with limited data.Advances in neural information processing systems, 34:25476–25488, 2021.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Mastering atari games with limited data.Advances in neural information processing systems, 34:25476–25488, 2021

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.606430Z

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Observation 78b34cf3-9b9d-4b81-85d1-48c022f7a2b8 · outbound

This paper cites A graph placement methodology for fast chip design.Nature, 594(7862):207–212, 2021.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One A graph placement methodology for fast chip design.Nature, 594(7862):207–212, 2021

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.584564Z

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Observation d14d986c-aa0b-4a88-a7b6-4179af0f8d06 · outbound

This paper cites Hierarchical reinforcement learning for scarce medical resource allocation with imperfect information.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Hierarchical reinforcement learning for scarce medical resource allocation with imperfect information

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.558171Z

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Observation 99e60500-87cd-4591-81ca-4282dfb822a5 · outbound

This paper cites Reinforcement learning enhances the experts: Large-scale covid-19 vaccine allocation with multi-factor contact network.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Reinforcement learning enhances the experts: Large-scale covid-19 vaccine allocation with multi-factor contact network

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.538975Z

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Observation f0e78dcd-1a14-4ab8-9db4-436492db1d43 · outbound

This paper cites Gat-mf: Graph attention mean field for very large scale multi-agent reinforcement learning.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Gat-mf: Graph attention mean field for very large scale multi-agent reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.520522Z

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Observation 1638ec47-1810-47be-9ac0-132adbf5f3bb · outbound

This paper cites Spatial planning of urban communities via deep reinforcement learning.Nature Computational Science, 3(9):748– 762, 2023.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Spatial planning of urban communities via deep reinforcement learning.Nature Computational Science, 3(9):748– 762, 2023

Reference 11

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This paper cites A survey of machine learning for urban decision making: Applications in planning, transportation, and healthcare.ACM Computing Surveys, 2024.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One A survey of machine learning for urban decision making: Applications in planning, transportation, and healthcare.ACM Computing Surveys, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.491251Z

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Observation 39339997-605b-4318-bbbf-bfec067c9de8 · outbound

This paper cites Dyps: Dynamic parameter sharing in multi-agent reinforcement learning for spatio-temporal resource allocation.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Dyps: Dynamic parameter sharing in multi-agent reinforcement learning for spatio-temporal resource allocation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.471478Z

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Observation 3652024f-61f0-4ba1-b24b-bf63788475f8 · outbound

This paper cites Coopride: Cooperate all grids in city-scale ride-hailing dispatching with multi-agent reinforcement learning.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Coopride: Cooperate all grids in city-scale ride-hailing dispatching with multi-agent reinforcement learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.453447Z

Source-reported events for the cited work

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Observation a86bb074-2a9b-47c2-90ba-95720d7c2bec · outbound

This paper cites A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning

Reference 15

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Observation 52045072-bd79-4cbd-a6a1-4dafc2302c9e · outbound

This paper cites How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments

Reference 16

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Observation 73c5c68b-f8d5-41cd-840a-450a3b80d005 · outbound

This paper cites Ensemble deep learning: A review.Engineering Applications of Artificial Intelligence, 115:105151, 2022.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Ensemble deep learning: A review.Engineering Applications of Artificial Intelligence, 115:105151, 2022

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.437638Z

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Observation 85e2220f-f902-4ad9-a9af-b91f988f89e4 · outbound

This paper cites A survey on ensemble learning.Frontiers of Computer Science, 14:241–258, 2020.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One A survey on ensemble learning.Frontiers of Computer Science, 14:241–258, 2020

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.421082Z

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Observation 422ef0c2-f114-4985-bf1d-8e18ca44d06a · outbound

This paper cites Ensemble reinforcement learning: A survey.Applied Soft Computing, page 110975, 2023.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Ensemble reinforcement learning: A survey.Applied Soft Computing, page 110975, 2023

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.405209Z

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Observation 69160df6-5acd-4af3-b01f-1599a827a21e · outbound

This paper cites Neural network ensembles in reinforcement learning.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Neural network ensembles in reinforcement learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.389623Z

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Observation 499cc08f-ef3e-4042-b788-fac959861c37 · outbound

This paper cites Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting

Reference 21

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Observation f5a597ba-0a2c-4a3b-b84f-36281b50f99a · outbound

This paper cites Ensemble algorithms in reinforcement learning.IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(4):930–936, 2008.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Ensemble algorithms in reinforcement learning.IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(4):930–936, 2008

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.374081Z

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Observation cc9b4d82-1585-4b67-a68d-64dd57681529 · outbound

This paper cites The arcade learning environment: An evaluation platform for general agents.Journal of Artificial Intelligence Research, 47:253–279, 2013.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One The arcade learning environment: An evaluation platform for general agents.Journal of Artificial Intelligence Research, 47:253–279, 2013

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.358572Z

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Observation 9e8f1398-449e-4f9f-a24d-73586e8337f3 · outbound

This paper cites Model-Based Reinforcement Learning for Atari.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Model-Based Reinforcement Learning for Atari

Reference 24

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Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Unresolved cited work

Reference 25

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This paper cites A survey of gpt-3 family large language models including chatgpt and gpt-4.Natural Language Processing Journal, page 100048, 2023.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One A survey of gpt-3 family large language models including chatgpt and gpt-4.Natural Language Processing Journal, page 100048, 2023

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.330048Z

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Observation 2cec5757-1406-40ba-8e63-a0a23a7a4af5 · outbound

This paper cites GPT-4 Technical Report.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One GPT-4 Technical Report

Reference 27

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This paper cites Evaluation of openai o1: Opportunities and challenges of agi.arXiv preprint arXiv:2409.18486, 2024.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Evaluation of openai o1: Opportunities and challenges of agi.arXiv preprint arXiv:2409.18486, 2024

Reference 28

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This paper cites Early access for safety testing.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Early access for safety testing

Reference 29

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Observation 7e419586-5f54-4b2f-97a6-d767d7a28875 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 30

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Observation 81c4a7cd-d620-47d9-aa37-efa2acf441b9 · outbound

This paper cites The Llama 3 Herd of Models.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One The Llama 3 Herd of Models

Reference 31

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This paper cites Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1– 113, 2023.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1– 113, 2023

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.296489Z

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This paper cites HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction

Reference 33

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Observation 208f9e6d-4ddf-402e-904d-6970c1895359 · outbound

This paper cites Stance detection with collaborative role-infused llm-based agents.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Stance detection with collaborative role-infused llm-based agents

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.274908Z

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Observation 9c59abd1-1c3a-417f-917f-fa74e16f8d15 · outbound

This paper cites A Survey of Large Language Models.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One A Survey of Large Language Models

Reference 35

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This paper cites A survey on evaluation of large language models.ACM Transactions on Intelligent Systems and Technology, 15(3):1–45, 2024.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One A survey on evaluation of large language models.ACM Transactions on Intelligent Systems and Technology, 15(3):1–45, 2024

Reference 36

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This paper cites Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models

Reference 37

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Observation 0feaeef7-b1aa-4336-a23b-c0835c105e9f · outbound

This paper cites Human-level control through deep reinforcement learning.Nature, 518(7540):529–533, 2015.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Human-level control through deep reinforcement learning.Nature, 518(7540):529–533, 2015

Reference 38

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Observation 64033e28-5714-48c8-ba06-23aa04f9f757 · outbound

This paper cites Deep reinforcement learning based ensemble model for rumor tracking.Information Systems, 103:101772, 2022.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Deep reinforcement learning based ensemble model for rumor tracking.Information Systems, 103:101772, 2022

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.226796Z

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Observation af3c0484-5644-417c-9949-b2722bfb93fe · outbound

This paper cites An oppositional-cauchy based gsk evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for covid-19 diagnosis.Applied Soft Computing, 111:107675, 2021.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One An oppositional-cauchy based gsk evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for covid-19 diagnosis.Applied Soft Computing, 111:107675, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.179323Z

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Observation fe683cb7-9a2a-4882-8a66-49e4a15e6b1d · outbound

This paper cites Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods

Reference 41

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Observation d068ec1c-548d-4d1b-8f5e-9195ddd2e1f5 · outbound

This paper cites Augmenting autotelic agents with large language models.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Augmenting autotelic agents with large language models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:01.080388Z

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Observation 5674d13e-b4ba-43fb-b7cc-48b26df7d801 · outbound

This paper cites Read and reap the rewards: Learning to play atari with the help of instruction manuals.Advances in Neural Information Processing Systems, 36, 2024.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Read and reap the rewards: Learning to play atari with the help of instruction manuals.Advances in Neural Information Processing Systems, 36, 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:00.823614Z

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Observation 1bfd96f7-721e-46bc-821e-f6528fd842e8 · outbound

This paper cites Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics

Reference 44

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Observation e6e1b00b-aa8c-4a42-a3a7-d870bb862034 · outbound

This paper cites Text2reward: Reward shaping with language models for reinforcement learning.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Text2reward: Reward shaping with language models for reinforcement learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:25:00.632744Z

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Observation 61197aeb-f728-4ac8-bfb1-da8bba5d30b2 · outbound

This paper cites LLM-Empowered State Representation for Reinforcement Learning.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One LLM-Empowered State Representation for Reinforcement Learning

Reference 46

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Observation dcc67497-61f2-4cda-bb79-433cc4f087b4 · outbound

This paper cites Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents

Reference 47

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This paper cites Exploration Situation.

Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One Exploration Situation

Reference 48

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