World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning | Paper Citation Record

Observation 321f9089-083b-497f-95ef-4144033a459e · outbound

This paper cites A comprehensive survey of multia- gent reinforcement learning.IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2):156–172, 2008.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning A comprehensive survey of multia- gent reinforcement learning.IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2):156–172, 2008

Reference 1

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Observation bd757bb4-8717-4ad2-a9e6-6ac6fb4270aa · outbound

This paper cites NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

Reference 2

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Observation 9c224c84-3df3-481f-8b90-4ac404bdc213 · outbound

This paper cites Reinforcement learning with human feedback for realistic traffic simulation.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Reinforcement learning with human feedback for realistic traffic simulation

Reference 3

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Observation bd7db8e7-3f78-4448-9232-211278db1a06 · outbound

This paper cites Safe-sim: Safety-critical closed-loop traffic simulation with diffusion-controllable adversaries.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Safe-sim: Safety-critical closed-loop traffic simulation with diffusion-controllable adversaries

Reference 4

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Observation d320f673-47b4-4b79-b7fc-c11bfc73f831 · outbound

This paper cites Rift: Group-relative rl fine-tuning for realistic and controllable traffic simulation.arXiv preprint arXiv:2505.03344, 2025.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Rift: Group-relative rl fine-tuning for realistic and controllable traffic simulation.arXiv preprint arXiv:2505.03344, 2025

Reference 5

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Observation 2e4d68c9-7a8d-408d-afaa-0ca9674fbcf1 · outbound

This paper cites PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving

Reference 6

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Observation e0613c43-f0fa-412c-af5a-3c897ab9ff72 · outbound

This paper cites Rethinking imitation-based planners for autonomous driving.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Rethinking imitation-based planners for autonomous driving

Reference 7

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Observation 066d82d0-58fc-480d-8b25-9d3b17c4d125 · outbound

This paper cites Training GANs with Optimism.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Training GANs with Optimism

Reference 8

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Observation 3ddaeb24-46a3-40fe-ba62-560f08f11f46 · outbound

This paper cites Parting with mis- conceptions about learning-based vehicle motion planning.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Parting with mis- conceptions about learning-based vehicle motion planning

Reference 9

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Observation 37de8caf-59e8-476d-a86b-e0eb8d30dae0 · outbound

This paper cites Artemis: Autoregressive end-to-end trajectory planning with mixture of experts for autonomous driving.IEEE Robotics and Automation Letters, 11(1): 226–233, 2025.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Artemis: Autoregressive end-to-end trajectory planning with mixture of experts for autonomous driving.IEEE Robotics and Automation Letters, 11(1): 226–233, 2025

Reference 10

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Observation 99592923-0e7e-438c-b472-fe66e85f939b · outbound

This paper cites Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment.Nature communications, 12(1):748, 2021.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment.Nature communications, 12(1):748, 2021

Reference 11

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Observation 7d0cc415-ddff-4f0a-8f7e-c70b0c61e829 · outbound

This paper cites Dense reinforcement learning for safety validation of autonomous vehicles.Nature, 615 (7953):620–627, 2023.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Dense reinforcement learning for safety validation of autonomous vehicles.Nature, 615 (7953):620–627, 2023

Reference 12

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Observation 123e2a70-0b74-4722-b69e-86e08609df01 · outbound

This paper cites Breaking through safety performance stagnation in autonomous vehicles with dense learning.Nature Communications, 2026.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Breaking through safety performance stagnation in autonomous vehicles with dense learning.Nature Communications, 2026

Reference 13

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Observation 4628c178-f537-4642-bc9c-3d39ff973857 · outbound

This paper cites Counterfactual multi-agent policy gradients.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Counterfactual multi-agent policy gradients

Reference 14

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Observation 8f4e4bbb-f234-49d3-9b08-8ffe450062f8 · outbound

This paper cites Foundation models in autonomous driving: A survey on scenario generation and scenario analysis.IEEE Open Journal of Intelligent Transportation Systems, 2026.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Foundation models in autonomous driving: A survey on scenario generation and scenario analysis.IEEE Open Journal of Intelligent Transportation Systems, 2026

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-14T10:22:08.922396Z

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Observation 6ba36404-4b44-4b75-bf62-44bcf5a9247c · outbound

This paper cites Variance reduction techniques for gradient estimates in reinforcement learning.Journal of Machine Learning Research, 5(Nov): 1471–1530, 2004.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Variance reduction techniques for gradient estimates in reinforcement learning.Journal of Machine Learning Research, 5(Nov): 1471–1530, 2004

Reference 16

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Observation 98eee0cb-3015-4926-8af4-d5f708a127cc · outbound

This paper cites Can vehicle motion planning generalize to realistic long-tail scenarios? In2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5388–5395.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Can vehicle motion planning generalize to realistic long-tail scenarios? In2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5388–5395

Reference 17

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Observation 4adf986f-a5c7-4610-b180-b812633c7125 · outbound

This paper cites King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients

Reference 18

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Observation 4b15b28d-4e43-4635-a43f-b25648193ddb · outbound

This paper cites Solving motion planning tasks with a scalable generative model.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Solving motion planning tasks with a scalable generative model

Reference 19

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Observation 00ed0c52-cdf5-4fa1-91bd-69524ca95813 · outbound

This paper cites Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving

Reference 20

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Observation 0f61e445-d593-4277-8f05-a95a3566c905 · outbound

This paper cites Gen-drive: Enhancing diffusion generative driving policies with reward modeling and reinforcement learning fine-tuning.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Gen-drive: Enhancing diffusion generative driving policies with reward modeling and reinforcement learning fine-tuning

Reference 21

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Observation 2d9c9bd4-626c-43ae-a5aa-07f7cb1b4754 · outbound

This paper cites Approximately optimal approximate reinforcement learning.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Approximately optimal approximate reinforcement learning

Reference 22

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Observation a708a591-4dcd-4842-b85b-ebd4ee3d31d3 · outbound

This paper cites Beyond behavior cloning in autonomous driving: a survey of closed-loop training techniques.Authorea Preprints, 2025.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Beyond behavior cloning in autonomous driving: a survey of closed-loop training techniques.Authorea Preprints, 2025

Reference 23

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Observation b8a5b70e-387b-4fc4-8ca1-829cc517e1df · outbound

This paper cites Plannerrft: Reinforcing diffusion planners through closed-loop and sample-efficient fine-tuning.arXiv preprint arXiv:2601.12901, 2026.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Plannerrft: Reinforcing diffusion planners through closed-loop and sample-efficient fine-tuning.arXiv preprint arXiv:2601.12901, 2026

Reference 24

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Observation 003f9d1b-9f34-4a2d-8566-d4fe067d065f · outbound

This paper cites Curse of rarity for autonomous vehicles.nature communications, 15(1):4808, 2024.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Curse of rarity for autonomous vehicles.nature communications, 15(1):4808, 2024

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-14T10:22:08.922396Z

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Observation 956d03d0-a960-423d-ba01-3c51b0155913 · outbound

This paper cites Adv-bmt: Bidirectional motion transformer for safety-critical traffic scenario generation.arXiv preprint arXiv:2506.09485, 2025.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Adv-bmt: Bidirectional motion transformer for safety-critical traffic scenario generation.arXiv preprint arXiv:2506.09485, 2025

Reference 26

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Observation e5bd4a86-69fb-4704-8f5e-14bfd5b93437 · outbound

This paper cites Llm-attacker: Enhancing closed-loop adversarial scenario generation for autonomous driving with large language models.IEEE Transactions on Intelligent Transportation Systems, 2025.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Llm-attacker: Enhancing closed-loop adversarial scenario generation for autonomous driving with large language models.IEEE Transactions on Intelligent Transportation Systems, 2025

Reference 27

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Observation 5349a390-d4e3-4fca-9512-1a4f8245fa82 · outbound

This paper cites The numerics of gans.Advances in neural information processing systems, 30, 2017.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning The numerics of gans.Advances in neural information processing systems, 30, 2017

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Observation 7a92c527-ae9e-4254-a376-df972b415a06 · outbound

This paper cites Steerable Adversarial Scenario Generation through Test-Time Preference Alignment.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Steerable Adversarial Scenario Generation through Test-Time Preference Alignment

Reference 29

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Observation 04d4b13b-4f96-48b7-acde-a2de901ebb6c · outbound

This paper cites Adv-0: Closed-loop min-max adversarial training for long-tail robustness in autonomous driving.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Adv-0: Closed-loop min-max adversarial training for long-tail robustness in autonomous driving

Reference 30

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Observation 90ac7391-8989-498e-9fa7-dac15a63c501 · outbound

This paper cites Advancing multi-agent traffic simulation via r1-style reinforcement fine-tuning.arXiv preprint arXiv:2509.23993, 2025.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Advancing multi-agent traffic simulation via r1-style reinforcement fine-tuning.arXiv preprint arXiv:2509.23993, 2025

Reference 31

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Observation 60f21c84-cb1e-4530-8369-4443545d5ac7 · outbound

This paper cites Improving agent behaviors with rl fine-tuning for autonomous driving.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Improving agent behaviors with rl fine-tuning for autonomous driving

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Observation 49e063c5-649e-4a6c-8952-3ce86a26b5eb · outbound

This paper cites Trajeglish: Traffic Modeling as Next-Token Prediction.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Trajeglish: Traffic Modeling as Next-Token Prediction

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Observation 74cb9b4a-eac8-4597-b9d1-343f09109855 · outbound

This paper cites Generating useful accident-prone driving scenarios via a learned traffic prior.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Generating useful accident-prone driving scenarios via a learned traffic prior

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Observation 4a1fd4dd-c60e-47ff-813e-1531ec603ba8 · outbound

This paper cites Optimization of conditional value-at-risk.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Optimization of conditional value-at-risk

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Observation ea798b9c-08c9-4e14-99a1-fa1776f9828a · outbound

This paper cites Urban driver: Learning to drive from real-world demonstrations using policy gradients.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Urban driver: Learning to drive from real-world demonstrations using policy gradients

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Observation 572122c2-2471-4b16-a8c3-47ee08cb1a3c · outbound

This paper cites Trust region policy optimization.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Trust region policy optimization

Reference 37

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Observation 33b5e601-1f74-4521-abff-20376574f704 · outbound

This paper cites Motionlm: Multi-agent motion forecasting as language modeling.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Motionlm: Multi-agent motion forecasting as language modeling

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Observation 08bb54b0-62ff-4be9-b0ab-aac6fe6f954c · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

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Observation 903b832a-bf37-415b-9ed7-b75974613e76 · outbound

This paper cites Seal: Towards safe autonomous driving via skill-enabled adversary learning for closed-loop scenario generation.IEEE Robotics and Automation Letters, 10(9):9320–9327, 2025.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Seal: Towards safe autonomous driving via skill-enabled adversary learning for closed-loop scenario generation.IEEE Robotics and Automation Letters, 10(9):9320–9327, 2025

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Observation 02d20eb9-5c0e-4e2e-80c4-12eb2bc818e2 · outbound

This paper cites Large Trajectory Models are Scalable Motion Predictors and Planners.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Large Trajectory Models are Scalable Motion Predictors and Planners

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Observation da7d93c8-fb70-4352-83f7-dd2175a62713 · outbound

This paper cites Flow matching-based autonomous driving planning with advanced interactive behavior modeling.arXiv preprint arXiv:2510.11083, 2025.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Flow matching-based autonomous driving planning with advanced interactive behavior modeling.arXiv preprint arXiv:2510.11083, 2025

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Observation ce0c139a-c412-4198-91b8-a4ffaedc0b4d · outbound

This paper cites Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling

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Observation 0b1cd810-a684-497b-bcd7-1b867e495bc2 · outbound

This paper cites Motion planning for autonomous driving: The state of the art and future perspectives.IEEE Transactions on Intelligent Vehicles, 8(6):3692–3711, 2023.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Motion planning for autonomous driving: The state of the art and future perspectives.IEEE Transactions on Intelligent Vehicles, 8(6):3692–3711, 2023

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Observation 4939d329-be0f-49cc-bb06-8b715a518ae8 · outbound

This paper cites Advsim: Generating safety-critical scenarios for self- driving vehicles.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Advsim: Generating safety-critical scenarios for self- driving vehicles

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Observation 1cf38c5b-beb7-4d03-853b-12576071ade7 · outbound

This paper cites Smart: Scalable multi-agent real-time motion generation via next-token prediction.Advances in Neural Information Processing Systems, 37:114048–114071, 2024.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Smart: Scalable multi-agent real-time motion generation via next-token prediction.Advances in Neural Information Processing Systems, 37:114048–114071, 2024

Reference 46

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Observation be889e97-a01b-4611-bae9-f7c82f5ef1b6 · outbound

This paper cites Diffscene: Diffusion-based safety- critical scenario generation for autonomous vehicles.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Diffscene: Diffusion-based safety- critical scenario generation for autonomous vehicles

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Observation 2dfb0e97-afd3-43ae-8333-6be8b692f659 · outbound

This paper cites Dap: A discrete-token autoregressive planner for autonomous driving.arXiv preprint arXiv:2511.13306, 2025.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Dap: A discrete-token autoregressive planner for autonomous driving.arXiv preprint arXiv:2511.13306, 2025

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Observation 9e5c05a6-eea7-4e6d-a928-de1daf92bea8 · outbound

This paper cites Carplanner: Consistent auto-regressive trajectory planning for large-scale reinforcement learning in autonomous driving.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Carplanner: Consistent auto-regressive trajectory planning for large-scale reinforcement learning in autonomous driving

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Observation 2ae657a7-ede1-44c9-a9cd-c80546993300 · outbound

This paper cites Epona: Autoregressive diffusion world model for autonomous driving.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Epona: Autoregressive diffusion world model for autonomous driving

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Observation 6fd8628e-4bdd-4dfe-9145-5fd09abb076b · outbound

This paper cites Cat: Closed-loop adversarial training for safe end-to-end driving.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Cat: Closed-loop adversarial training for safe end-to-end driving

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Observation 9ef54a62-2815-496b-bf95-bd8a5652323f · outbound

This paper cites Closed-loop supervised fine-tuning of tokenized traffic models.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Closed-loop supervised fine-tuning of tokenized traffic models

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Observation 85acace4-8496-4423-9f81-41a2522d474c · outbound

This paper cites Diffusion-Based Planning for Autonomous Driving with Flexible Guidance.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Diffusion-Based Planning for Autonomous Driving with Flexible Guidance

Reference 53

Resolution: unresolved; no resolver link, observed 2026-07-14T10:22:08.922396Z

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Observation fba96190-e5d0-4dd7-be0f-f66252df5422 · outbound

This paper cites Behaviorgpt: Smart agent simulation for autonomous driving with next-patch prediction.Advances in Neural Information Processing Systems, 37:79597–79617, 2024.

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning Behaviorgpt: Smart agent simulation for autonomous driving with next-patch prediction.Advances in Neural Information Processing Systems, 37:79597–79617, 2024

Reference 54

Resolution: malformed identifier; no resolver link, observed 2026-07-14T10:22:08.922396Z

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