# Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving

arXiv: https://arxiv.org/abs/2409.18343 · HTML: https://mlsanity.com/paper/2409.18343/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 34 of its 43 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 30 of 34)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Training Diffusion Models with Reinforcement Learning](https://mlsanity.com/paper/2305.13301/) (2023)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://mlsanity.com/paper/2302.09419/) (2023)
- [MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning](https://mlsanity.com/paper/2109.12674/) (2021)
- [Wayformer: Motion Forecasting via Simple & Efficient Attention Networks](https://mlsanity.com/paper/2207.05844/) (2022)
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors](https://mlsanity.com/paper/2101.06557/) (2021)
- [Guided Conditional Diffusion for Controllable Traffic Simulation](https://mlsanity.com/paper/2210.17366/) (2022)
- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023)
- [MotionLM: Multi-Agent Motion Forecasting as Language Modeling](https://mlsanity.com/paper/2309.16534/) (2023)
- [MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying](https://mlsanity.com/paper/2306.17770/) (2023)
- [Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios](https://mlsanity.com/paper/2212.11419/) (2022)
- [SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving](https://mlsanity.com/paper/2010.09776/) (2020)
- [TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios](https://mlsanity.com/paper/2210.06609/) (2022)
- [A Review of Off-Policy Evaluation in Reinforcement Learning](https://mlsanity.com/paper/2212.06355/) (2022)
- [Benchmarks for Deep Off-Policy Evaluation](https://mlsanity.com/paper/2103.16596/) (2021)
- [The Waymo Open Sim Agents Challenge](https://mlsanity.com/paper/2305.12032/) (2023)
- [SimNet: Learning Reactive Self-driving Simulations from Real-world Observations](https://mlsanity.com/paper/2105.12332/) (2021)
- [ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling](https://mlsanity.com/paper/2306.12241/) (2023)
- [Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world](https://mlsanity.com/paper/2206.09889/) (2022)
- [TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction](https://mlsanity.com/paper/2303.04116/) (2023)
- [UniWorld: Autonomous Driving Pre-training via World Models](https://mlsanity.com/paper/2308.07234/) (2023)
- [PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation](https://mlsanity.com/paper/2109.11094/) (2021)

## Built on by (most cited first, 27 of 27)

- [Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models](https://mlsanity.com/paper/2412.05334/) (2024)
- [SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model](https://mlsanity.com/paper/2506.21976/) (2025)
- [Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends](https://mlsanity.com/paper/2506.20966/) (2025)
- [Advancing Multi-agent Traffic Simulation via R1-Style Reinforcement Fine-Tuning](https://mlsanity.com/paper/2509.23993/) (2025)
- [UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation](https://mlsanity.com/paper/2501.17015/) (2025)
- [medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support](https://mlsanity.com/paper/2505.19785/) (2025)
- [Test-time Offline Reinforcement Learning on Goal-related Experience](https://mlsanity.com/paper/2507.18809/) (2025)
- [Learning from Mistakes: Post-Training for Driving VLA with Takeover Data](https://mlsanity.com/paper/2603.14972/) (2026)
- [World Engine: Towards the Era of Post-Training for Autonomous Driving](https://mlsanity.com/paper/2606.19836/) (2026)
- [SPACeR: Self-Play Anchoring with Centralized Reference Models](https://mlsanity.com/paper/2510.18060/) (2025)
- [DecompGAIL: Learning Realistic Traffic Behaviors with Decomposed Multi-Agent Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2510.06913/) (2025)
- [RIFT: Group-Relative RL Fine-Tuning for Realistic and Controllable Traffic Simulation](https://mlsanity.com/paper/2505.03344/) (2025)
- [Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving](https://mlsanity.com/paper/2509.02754/) (2025)
- [Human-like autonomy emerges from self-play and a pinch of human data](https://mlsanity.com/paper/2606.19370/) (2026)
- [SceneStreamer: Continuous Scenario Generation as Next Token Group Prediction](https://mlsanity.com/paper/2506.23316/) (2025)
- [Post-Training and Test-Time Scaling of Generative Agent Behavior Models for Interactive Autonomous Driving](https://mlsanity.com/paper/2512.13262/) (2025)
- [SCORP: Scene-Consistent Multi-agent Diffusion Planning with Stable Online Reinforcement Post-Training for Cooperative Driving](https://mlsanity.com/paper/2604.11734/) (2026)
- [RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning](https://mlsanity.com/paper/2605.19033/) (2026)
- [Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety](https://mlsanity.com/paper/2503.00077/) (2025)
- [LaViPlan : Language-Guided Visual Path Planning with RLVR](https://mlsanity.com/paper/2507.12911/) (2025)
- [Agent Fine-tuning through Distillation for Domain-specific LLMs in Microdomains](https://mlsanity.com/paper/2510.00482/) (2025)
- [World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning](https://mlsanity.com/paper/2607.10630/) (2026)
- [CounterPlay: Counterfactual Post-Training for Self-Play Driving Policies](https://mlsanity.com/paper/2609.21617/) (2026)
- [LayerMix: Enhanced Data Augmentation through Fractal Integration for Robust Deep Learning](https://mlsanity.com/paper/2501.04861/) (2025)
- [CORTEX-AVD: A Framework for CORner Case Testing and EXploration in Autonomous Vehicle Development](https://mlsanity.com/paper/2504.03989/) (2025)
- [Constructing the Umwelt: Cognitive Planning through Belief-Intent Co-Evolution](https://mlsanity.com/paper/2511.05540/) (2025)
- [MAGNIFIED: RL Fine-tuning of Multimodal Large Language Models for Motion Planning](https://mlsanity.com/paper/2606.20641/) (2026)

## Abstract (arXiv)

A major challenge in autonomous vehicle research is modeling agent behaviors, which has critical applications including constructing realistic and reliable simulations for off-board evaluation and forecasting traffic agents motion for onboard planning. While supervised learning has shown success in modeling agents across various domains, these models can suffer from distribution shift when deployed at test-time. In this work, we improve the reliability of agent behaviors by closed-loop fine-tuning of behavior models with reinforcement learning. Our method demonstrates improved overall performance, as well as improved targeted metrics such as collision rate, on the Waymo Open Sim Agents challenge. Additionally, we present a novel policy evaluation benchmark to directly assess the ability of simulated agents to measure the quality of autonomous vehicle planners and demonstrate the effectiveness of our approach on this new benchmark.