AutoDrive-R²: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving
Authors: Zhenlong Yuan
Chengxuan Qian
Jing Tang
Rui Chen
Zijian Song
Lei Sun
Xiangxiang Chu
Yujun Cai
Dapeng Zhang
Shuo Li
Venue: ICLR 2026 Poster (2026)
Keywords: Applications
Robots
Vision–Language–Action Models
Primary area: applications to robotics, autonomy, planning

Abstract: Vision–Language–Action (VLA) models in autonomous driving systems have recently demonstrated transformative potential by integrating multimodal perception with decision-making capabilities. However, the interpretability and coherence of the decision process and the plausibility of action sequences remain largely underexplored. To address these issues, we propose AutoDrive-R², a novel VLA framework that enhances both reasoning and self-reflection capabilities of autonomous driving systems through chain-of-thought (CoT) processing and reinforcement learning (RL). Specifically, we first propose an innovative CoT dataset named nuScenesR²-6K for supervised fine-tuning, which effectively builds cognitive bridges between input information and output trajectories through a four-step logical chain with self-reflection for validation. Moreover, to maximize both reasoning and self-reflection during the RL stage, we further employ the Group Relative Policy Optimization (GRPO) algorithm within a physics-grounded reward framework that incorporates spatial alignment, vehicle dynamic, and temporal smoothness criteria to ensure reliable and realistic trajectory planning. Extensive evaluation results across both nuScenes and Waymo datasets demonstrates the state-of-the-art performance and robust generalization capacity of our proposed method.

Published as a conference paper at ICLR 2026
AUTODRIVE-RE INCENTIVIZING REASONING AND SELF-REFLECTION CAPACITY FOR VLA MODEL IN AUTONOMOUS DRIVING
Zhenlong Yuan!*!, Chengxuan Qian'", Jing Tang', Rui Chen', Zijian Song", Lei Sun Xiangxiang Chu', Yujun Cai', Dapeng Zhang Shuo Li
AMAP, Alibaba Group, Sun Yat-sen University, University of Queensland, Lanzhou University, Case Western Reserve University
Equal contribution | Project Lead
Corresponding Author
ABSTRACT
Vision-LanguageAAcionn (VLA) models in autonomous driving systems have re- cently demonstrated transformative potential by integrating multimodal pereep- tion with decision-making capabilities. However, the interpretability and coher- ence of the decision process and the plausibility of action sequences remain largely underexplored. To address these issues, propose AutoDrive-R?, novel VLA framework that enhances both reasoning and self-reflection capabilities of au- tonomous driving systems through chain-of-thoughh (CoT) processing and rein- forcement learning (RL). Specifically, we first propose an innovative CoT dataset named nuScenesR2-6K for supervised fine-tuning, which effectively builds cogni- tive bridges between input information and output trajectories through four-step logical chain with self-reflection for validation. Moreover, maximize both rea- soning and self-reflection during the RL stage, we further employ the Group Rela- tive Policy Optimization algorithm within a physies-grounded reward framework that incorporates spatial alignment, vehicle dynamic, and temporal smoothness criteria to ensure reliable and realistic trajectory planning. Extensive evaluation results across both nuScenes and Waymo datasets demonstrates the state-of-the- art performance and robust generalization capacity our method.
Auto Drive-n
Predicted future movemont for the next secands.
QWENZ 5-VL
AuteDrive-R
Question:
Qwwen2.5WV VL 1e
3..9, 8013 (855.085)
Performance Nuscenns
autonomous
You are on
EMMA-
Buok nadj 22..066 14.5 600
Your task
driving agent.
AutoDrive-2
cuiiddo Observatio:
abood
your
iecumarty walking veliclo free
tettlll light
nemond, and podnarian
AsteDrie.al
waypoints
Enformation:
Calosbeion Apply the kinameiz
emeosali, the vehiclo
predict fiture positimns
Zero-shot Performance Wayme
the next timesteps, given the vehicle
the vehicle over
Accelenction:
(1-0.5s)l-22, 0.02]
3. Logical Dedections: Although tiem is pedession cresing, the vebkle must hult due the rnd light. regarillee of the pedetriar's movemntt
tee velicle Since velicee must
seed fx cakulation.
Qees2 Qeex2.5-V1. 5-vi.
2.13
- Y m//"2.
Velecity: 4
alleres to Refleciion Ensure fax predicted injectory collaates-
Driec-LLA VLM
0.42
intent inforred
images,
Steering angle:
de lase marking and the roed nics <atauo
Casseert (0.0. 300, (00 0.0000.0 8,30,00 aogooo Geleee
AuNDDriv-RR
0.20
Work done during the internship at AMAP, Alibaha Group.

---

Published as a conference paper at ICLR 2026
1 INTRODUCTION
Autonomous driving technologies have witnessed rapid advancements in recent years. These systems typically take sensor data input and then output planning trajectories. Traditional pipelines (Kendall et al., 2019; Chen et al., 2021) usually adopt architectures with separate percep- tion, mapping, prediction, and planning modules. Such design may suffer from two key limitations: error accumulation and lack joint optimization across components, leading to performance degra- dation. In contrast, modern methods (Hu et al., 2023; Jiang et al., 2023; Chen et al., 2024) unify these complex systems into a single end-to-end paradigm, which naturally offers three main benefits: system simplification, enhanced robustness, and alleviated error accumulation.
However, these end-to-end methods primarily focus on trajectory planning while lacking the con- textual reasoning necessary for complex driving scenarios. To address this limitation, recent works integrate Vision-Language Models (VLMs) into autonomous driving systems, leveraging their pre- trained reasoning capabilities to enhance decision-makee making in challenging situations (Shao et al., 2024a; Wang et al., 2023; Tian et al., 2024). Unlike traditional approaches that train perception- policy modules from scratch, VLM-based methods instead fine-tune pre-trained models by lever- aging pre-training situations and develop sophisticated navigation strategies. Despite promising results, current VLM- based systems still struggle with consistently producing accurate planning outputs.
millions of image-text pairs, enhancing vehicles to interpret dynamic traffic
Building upon VLMs, to final action prediction, enabling robots and autonomous vehicles to generate precise actions from visual inputs and textual instructions ( (Yang et al., 2025). This advancement has led to the adoption of similar action generation mechanisms in autonomous driving, with approaches like TO (Black al., 2024) inspiring the development of action tokenizer that produce precise planning trajectories (Zhou al., 2025b).
Language-AAttini models (VLA) further extend reasoning capabilities
However, current VLA approaches in autonomous driving typically face two critical limitations that hinder their practical deployment: First, existing trajectory generation framework often pro- duce physically infeasible outputs. Existing approaches that directly generate textual commands or waypoints via VLMs frequently produce
-ineesible outputs and exhibit model collapse. While intermediate representations like meta-actions or latent action tokens have been proposed to mitigate these issues, these designs violate the end-to-end optimization principle and significantly increase model complexity overhead. Second, current systems demonstrate inadequate reasoning capabilities for complex driving scenarios. Since most methods employ simplistic reasoning strate- gies, they fail to account for both complicated road condition and vehicle kinematic constraints, resulting predicted trajectories significantly deviate from real-world requirements. These limita- tions underscore the critical need for a novel VLA framework that balances architectural simplicity, robust contextual understanding, and strict physical constraints.
To overcome these challenges, propose AutoDrive-R?, novel VLA framework that enhances both reasoning quality and physical feasibility through a two-stage training approach. Our key insight is that effective autonomous driving requires structured reasoning processes that can be sys- tematically validated and refined. Specifically, to address the inadequacy of contextual reasoning for complex driving scenarios, we first construct nuScenesR2-6K, for supervised fine-tuning (SFT). nuScenesR2-6K is the first dataset in autonomous driving that stimulates both reasoning and self-refiection capabilities for VLA models. Unlike prior datasets, nuScenes provides not only ground-truth trajectories but also the underlying reasoning and self-reflection steps, ensuring both the correctness and causal plausibility of driving behavior.
thain-of-tthouhh (CoT) dataset
Furthermore, to resolve the challenge of physically infeasible trajectory generation, we further de- velop a -rooudd-grounded reward framework tailored to group relative policy optimization (GRPO) of autonomous driving tasks. By explicitly incorporating spatial alignment, vehicle dynamic and temporal smoothness constraints into considcration, our physics-grountnn ment learning to adapt to diverse driving scenarios and vehicle dynamics while maintaining physical feasibility and motion comfort. Comprehensive experiments on nuScenes and Waymo benchmarks demonstrate that AutoDrive-R2 achieves tfte-off-the-an performance. Our key contributions are:
reward enables reinforce-
We introduce AutoDrive-RR a novel VLA framework that enables semantic reasoning with self- reflection step and trajectory planning from visual information and language instructions.
2

---

Published as a conference paper at ICLR 2026
Supervised Fine-tuning
Information:
Prompt:
Four-step logical chale:
Chan-of-thoug
@ O 7
Snap-by-sthg
Step-tyctip
5o...a).:.. 202)
<thako
x 4..1 V 0.3 race".
Voua ordyyte of Image
I. The lone meriongs suppert ealigtt cime to the leh...
Steemi 0.33
L.sservation
C.Colculstion
2. xp-- 4thy 0.505 90
Logicel deductinnt
valden chocks
4. Valcsttor There are no abonos the vchile path -trado
Question:
(6777 1941,19 19 0-492 [S54, AAI, 9
vehill the timestex
future nopo ets
sanoue sprt, Vi) stonswers
Saage
Refllactoon
Qwen2.5-VL
Sudee AT Sagn
C-T-SFT
Stage Two
AutoDrive-R
2.Reinforcement Learning
Policy
KL Loss
Refenence Model
daaking lehward tare graburly of the rek Paflecion E Enaure thecthe preditted troletory
velullaae nowing
0.077, 1--
Peplce-rrantte Accunayy Rewand:
copaaa ouannatirr ard the hoad essight sead
2.33 2.73
Algesant
Dir-wr- WW
ferwer without dignl changes
dgal
equation
0,95
Valkle
Di
and mnnctt the
Samard
wine served Oven sppaaac digit
2- 0011
(-0.5))
Tempera!
0.229
2.0 20..va....3
goodede sliting
Figure 1: Pipeline of our method. We adopt two-stage training process. The first stage introduces an innovative CoT dataset named nuScenesk2-6K for SFT. The nuScenesk2-66 adopts a four-step logical chain with self-reflection generate valuable chain-of-thoughh data. The second stage pro- poses an novel physics-groundee reward framework for RL optimization, which incorporates spatial aligmment, vehicle dynamic, and temporal smoothness for reliable trajectory planning.
We propose nuScenesR2-6i -66, innovative CoT dataset adopting four-step logic chain with self-reflection to help build foundational perception capabilities after SFT. We propose RL-based post-training method based on GRPO, which incorporates physics- grounded rewards as constraint to refine planning trajectory for diverse scenes.
2 METHODOLOGY
Overview. In this section, we present an overview of AutoDrive-R2 The target of trajectory plan- ning task requires the model to forecast a vehicle's 's future motion based on its historical sensor data and contextual information. Formally, given a sequence of historical vehicle states H (including position, acceleration, velocity, steering angle, etc.) and its camera image F, the model M out- puts predicted bird s-yde view (BEV) trajectory coordinates T' over the next 3 seconds at 0.5-second intervals, defined as T = F).
As depicted in Fig. 2. our training process contains two stages. In stage one, we construct a high- quality dataset nuScenesR2-6K for cold start to build cognitive bridges between input information and output trajectories through four-step logical chain with self-reflection for validation. In stage two, we employ a physics-based reinforcement learning framework that integrates spatial alignment, vehicle dynamic and temporal smoothness ensure physically feasible trajectory generation.
2.1 LOGISTIC COT DATASET WITH SELF-REFLECTION
The success of VLA models in autonomous driving critically depends on their ability to produce both interpretable reasoning and physically feasible actions. However, existing training approaches often fail to establish this dual requirement, leading to models that either lack explainable decision- making processes or generate unrealistic trajectories. To investigate this challenge, we initially ex- plored direct reinforcement learning optimization for trajectory planning, following recent advances in reasoning- baased RL (Guo et al., 2025). However, preliminary experiments revealed that mod- els trained exclusively on RL exhibited significant degradation in trajectory planning compared to models pre-trained with SFT before RL. Therefore, we constructed a nigh-quality cold-start dataset named nuScenesR266K to cultivate the model's foundational understanding of trajectory planning.
3

---

Published as a conference paper at ICLR 2026
To this end, we constructed nuScenesR2-6K, dataset of 6,000 image-trajectte pairs enriched with high-fidelity tfain-of-thought (CoT) reasoning. The dataset was created through a meticulous "generate-then- -haee--hiindvteee pipeline. First, curated an initial pool of approximately 8,000 image- trajectory pairs from the nuScenes training set. We then leveraged the Qwen2.5-V1.-77 VLL-722 model to synthesize initial CoT reasoning sequences for these pairs. Crucially, ensure data quality, employed the closed-source Qwen-VL-Max model as an expert validator to score and review each generated chain. Chains containing factual errors or logical inconsistencies were systematically discarded. This rigorous filtering process yielded the final 6,000 high-fidelity samples for SFT.
Moreover, we observe that many existing approaches rely on universal prompts for problem-to- answer reasoning, lacking structured guidance for rational analysis. While this strategy proves ef- fective for simple tasks, frequently fails when confronted with complex mathematical or logical problems. To address this limitation, our CoT prompt design systematically decomposes trajectory planning into three interdependent reasoning stages:
Mmage-Driveen Analysis: Establishing foundational scene understanding (e.g., obstacle and lane localization, traffic sign detection) to anchor subsequent reasoning.
Physics-based Calculation: Leveraging kinematic equations (e.g.. angular momentum conserva- tion) to translate abstract observations into quantifiable predictions.
Contextual Logic Synthesis: Integrating Nomain-speciff knowledge (c.g., intersection traffic rules) to ensure predictions align with real-world driving regulations.
To further enhance robustness and the correctness of answers, explicitly introduce self- reflection phase as the fourth step, inspired by mathematical reasoning frameworks that validate conclusions through backward-checking This allows the model to verify the coherence of its rea- soning and correct potential contradictions. Our prompt implements a four-step logic chain:
Observation Caleulation - Logic + Reflection,
which delivers both systematic and error-resilient reasoning. Ultimately, the nuScenesR2-6 dataset is adopted for supervised fine-tuning Qwen2-VL. VL-7B model, thus yielding our stage-1 model.
2.2 GROUP RELATIVE POLICY OPTIMIZATION (GRPO)
We follow the GRPO algorithm (Jiang et al., 2025) to train the model. Unlike traditional approaches that rely on critic networks estimate value functions, GRPO introduces pairwise comparison mechanism among candidate response. This design not only simplifies the architecture but also reduces computational overhead during training. The methodology begins by generating G distinct candidate responses {or..... oG for given input question through policy sampling. For our specific task, we implement rule-based verifiable reward functions assess response quality:
Accuracy Reward To better adapt to trajectory planning task, we define a physics-groundee ac- curacy rewards Feee which integrates spatial, kinematic, and temporal constraints for evaluation. Details are specified in the following section.
Format Reward The format reward Face enforces strict adherence the required out- put format. The model must produce responses in the form: "<think>>tinking process 31). ....(En, (tth.//////a </answer""""
value of is assigned if the format is correct, otherwise O. In summary, the total reward for response is calculated as: r + - To quantify the relative quality of all responses (ri,..., rr..., GRPO normalizes these scores by subtracting the group mean and dividing by the standard deviation. Consequently, the advantage for each response can be formulated by:
hhrre//hinb-<cmsserr/fr
F: mean (r.)e
A;
std 12
where A; is the relative advantage the i-th answer. Then the optimization objective further incor- porates regularization term ensure the updated policy me remains close to the original reference
4

---

Published as a conference paper at ICLR 2026
policy Tee. This is achieved by adding KL-divergence term D [I.) to the loss function:
(ORPO(!) -000====)-
(2)
min (T((=l))) Adu, clip
To(0:19)
e, 1 + Adv;) -DDx(Toll
Toota(oi(4)
TBald(o:)g)
D Dx (Toll Trer(oilg) log
Tre Tre (o:1)) 1,
T0(0:11)
(0:1g)))
where B acts as hyperparameter to trade-off between exploration and stability during optimization.
2.3 PHYSICS-GROUNDER ACCURACY REWARDS IN GRPO
In autonomous driving, traditional reward function designs often focus solely on trajectory position error, while neglecting the complex constraints in geometric, dynamical, and temporal dimensions. To address this issue, we propose a bhysics-grounder reward framework that integrates spatial align- ment, vehicle dynamics, and temporal continuity to comprehensively guide the model in generating safe, feasible, and comfortable driving strategies. This multi-dimensional approach not only ensures geometric accuracy but also explicitly incorporates the physical limitations of real-world vehicles and the perceptual requirements for motion smoothness, creating holistic optimization objective.
Spatial Alignment: Balancing Global Maneuverability. The foundation of any trajectory reward function lies in its ability to align predicted paths with target routes. We define a spatial accuracy term pos as the mean squared Euclidean distance between predicted and ground truth coordinates:
Σ
((x - x=gg + (1²² 3)
(4)
where N denotes the number of time steps, I', y² represent predicted coordinates at the i-th time step, while zigu vy correspond the ground-truth values. This formulation prioritizes global path ad- herence by penalizing deviations across all time steps, ensuring the vehicle remains on the intended route. However, focusing only on minimizing position error may produce physical-implausibee results. For instance, strictly following the shortest path might bring about abrupt steering or celeration, which not only violate vehicle kinematics but also compromise passenger comfort. To balance geometrie precision with practical feasibility, introduce additional constraints derived from vehicle dynamics.
Vehicle Dynamics: Bridging Perception and Control. Autonomous driving systems must respect the real-world physical limitations, which are governed by steering kinematics and longitudinal dynamics. Ignoring them may result in trajectories that impossible to execute (e.g., requiring infinite torque for abrupt steering changes) or uncomfortable for passengers. To ensure kinematic feasibility, penalize deviations in steering angles through the following term rac:
Σ (0)
0 )²)
stte
N
where 0 and 6g respectively denotes the predicted and corresponding ground-truth steering angle at j-th time step. Additionally, we address unphysical acceleration/braking patterns by introducing an additional velocity constraint term:
Fvel Σ (u* 22²22
(6)
where o and ogg respectively represents the predicted and corresponding ground-truth velocity the k-th time step. In summary, both Tae and Ivel enforce compliance with vehicle-specific con- straints, ensuring generated trajectories both physically realizable and socially acceptable in
5

---

Published as a conference paper at ICLR 2026
Table 1: Trajectory L2 errors and collision rates on the nuScenes dataset.
L2 Error (m) 4
Collision Rate (%) }
Method
Is
2s
3s
Avg.
Is
2s
38
Avg.
Open-source Generalist Vision Language Models
Llama-3.2-4 B-B- -iiiii-nnnon (Touvron et al., 2023)
1.54
3.31
3.91
2.92
DeepSeek- DeepSeek-VL22166t VL2-16B (Liu al., 2025)
0.66
1.68
2.92
1.75
LLaMA-3.2-1 IB- Vision (Touvron et al., 2023)
0.52
1.42
2.68
1.54
Twen-2.5-VL-3B (Bai et al., 2025)
2.69
4.16
5.78
4.21
0.32
0.54
0.78
0.55
Swen-2.5-VL-7B (Bai et al., 2025)
0.46
1.33
2.55
1.45
0.10
0.18
0.24
0.17
Training-baxer Driving Specialists (Existing Methods)
UniAD (Hu el al., 2023)
0.42 0.64 0.91 0.66
0.62
0.58
0.63
0.61
VAD (Jiang et al., 2023)
0.17 0.34 0,60 0.37
0.07
0.17
0.41
0.22
BEV-Planner (Li et al., 2024)
0.16 0.32 0.57 0.35 0.00
0.29
0.73 0.34
Ego-MLP (Zhai et al., 2023)
0.15 0.32 0.59 0.35
Ours and Key Competitors (Specialized Driving Models)
DLi (Tian et al., 2024)
0.18 0.34
0.68 0.40
0.10
0.22
0.45
0.27
OmniDrive (Wang et al., 2024)
0.14 0.29 0.55
0.33
0.00
0.13
0.78
0.30
DiveVL.-DDal (Tian et al., 2024)
0.15 0.29 0.48
0.31
0.05
0.08
0.17 0.10
EMMA (Hwang et al., 2024)
0.14 0.29 0.54
0.32
EMMA+ (Hwang et al., 2024)
0.13 0.27 0.48
0.29
Imprompt-VEL VLA
0.13 0.27 0.53 0.30
AutoDrive-RP 3B
0.35 0.49
0.62
0.49
0.13
0.44
0.87
0.48
AutoDrive-R* 7B
0.13 0.19 0.25
0.19
0.00
0.07
0.12 0.07
mixed traffic scenarios. These constraints explicitly bridge the gap between perception- driven plan- ning and actuator-level control, ensuring that predicted trajectories align with the physical bound- aries while maintaining ride quality.
Temporal Smoothness: Ensuring Navigation Reliability Temporal discontinuities in trajectory predictions fundamentally undermine the reliability of autonomous driving systems. When steering or acceleration commands exhibit sudden jumps between time steps, the predicted trajectories may lose coherence, which further compromises the system's 's ability to maintain stable, predictable mo- tion patterns required for safe navigation. To address this, we introduce a temporal smoothness term Trom that penalizes rapid variations in consecutive control signals:
N
N Σ
Tiem Σ
(0' 00 1
k o-1)?
Such design ensures temporal coherence of predicted trajectories. By explicitly constraining the rate of change in both steering and velocity, the reward function filters out unstable oscillations that could destabilize the vehicle's state estimation. This regularization effect strengthens the model's ability to generalize across diverse driving scenarios while maintaining safety margins during execution.
Integrated Reward Function. The final reward synthesizes all dimensions with learnable weights:
Face
Tpos + Aste Fate + Agel Fivl + Apem Apos
Tiem-
Here, Apos Astes Avel, Ase are learnable coefficients that balance trade-offs between competing ob- jectives. We set them all equal one in experiments. This holistic formulation ensures the model generates trajectories that are geometrically accurate, dynamically feasible, and temporally smooth, addressing the multifaceted challenges of autonomous driving.

---

Published as a conference paper at ICLR 2026
3 EXPERIMENT
3.1 EXPERIMENTAL SETTINGS
Datasets. For training, we adopt nuScenesR dataset, which contains 6k image-trajectory sam- ple pairs, each includes front-view image and 3-second trajectory planning with 0.5-second inter- vals. The Qwen2.5-VLL VL-7BB model is fine-tuned on these samples for SFT to establish foundational perception capabilitiis before RL. For evaluation, our method is tested nuScenes and Waymo datasets, both offering comprehensive autonomous driving data. The nuScenes dataset contains 1,000 urban driving scenes with six synchronized camera views to support planning tasks. Waymo dataset includes 4,021 driving segments, capturing eight camera views and ego- vehicle trajectories.
Details. We implement experiments on both Qwen2.5-VL-3B and Qwen2.5-VL-7B models. In both stages, the learning rate is set to 5e-7 with an accumulated total batch size of 8. The GRPO configured with maximum completion length of 4,096 tokens and samples responses per input. For training. the reinforcement learning (RL) pipeline utilized the TRL framework, and was executed for a total of 750 training iterations and I epoch for 18 hours.
Evaluation Metries. We evaluate performance using both accuracy and safety metrics, For accu- racy, we adopt the 1.2 distance (in meters) between the predicted and ground truth trajectories at 1s, 2s, and 3s, along with the average error. To assess safety, we also report the Collision Rate (%), which measures the frequency of collisions in the predicted paths. For all models, we utilize the of- ficial checkpoints and conduct evaluations under the same evaluation codes to ensure fairness. Note that the best and second-best results are highlighted in all tables.
3.2 EVALUATION RESULTS
Results on nuScenes Datasets Table 1 compares the prediction errors among our method and isting approaches on the nuScenes dataset. Notably, our approach consistently achieves the best performance across all time intervals, surpassing current leading methods such as EMMA+, which are trained on substantially larger internal datasets with 103k scenarios. In contrast, our training data consists of only 6k curated CoT samples for stage 1 and another 6k for stage 2, approximately 11.65% the size of EMMA+'s ' dataset. Furthermore, our model demonstrates significant improve- ments over Qwen2 Qwen2-VL-7B, reducing L2 errors by 86.9% despite having less parameter.
Zero-shot performance on Waymo Datasets Moreover, Tab. demonstrates the robust zero- shot capabilities of our model. Specifically, our method respectively reduces L2 errors by 33.3% and 90.7% compared to the latest EMMA+ method and Qwen2-VL-72B baseline models. Overall, model consistently delivers precise trajectory predictions across multiple datasets, es- tablishing its state-of-the-ard performance and gen- cralization capability.
Table 2: Trajectory 1.2 errors on Waymo.
L2 Error (m)
J
Method
1s
28
38
Avg.
Generalist VLMs Specialized Driving Models
Qwen-2.5-VL-3B
2.98
5.05 7.38
5.14
Qwen-2.5-VL-7B
1.66 1.82 2.92
2.13
DriveVLM
0.17 0.34 0.75
0.42
EMMA
0.12
0.28 0.61
0.34
Model Size In Tab. and Tab. we compare 3B and 7B variants of Qwen2.5-VL. within our two- stage training framework analyze impact of dif- ferent model size. While the 7B model achieves superior performance with an average L2 error of 0. 0.1,m, the 3B version demonstrates notable improvement than its baseline. The disparity high- lights that larger models inherently capture more complex patterns, but the two-stage framework (SFT GRPO) effectively compensates for the 3B model's limited capacity by enforcing strict tra- jectory constraints and contextual logic synthesis.
EMMA+
0.11 0.25 0.53
0.30
AutoDrive-R 3B
0.23
0.36
0.51
0.37
AutoDrive-R 7B 00.11 0.19 0.29
0.20
Closed-loop Experiment To evaluate our model's practical decision-making conducted closed- loop experiments on the NAV benchmark. As detailed Table 3, our method sets new of model achieves
the-art by outperforming all prior approaches across every metric. Specifically, top scores in safety, with Navigation Completion of 98.3 and Time-to-Collisiin of 95.6, while also excelling in driving quality by leading in Driving Agent Comfort (94.4) and Ego- Progress (81.6).
7

---

Published as a conference paper at ICLR 2026
QuenZ tc.
ENMM-
Auhoni R
Auho 2
00
Figure Qualitative comparison of trajectory planning performance across Siven2.5VVL-7 EMMA+, and AutoDrive-R2 the nuScenes dataset. Note that blue lines denote predicted trajectories while green lines represent ground truth trajectories.
The significant advantage observed in the Planner-Diverse Motion Score (PDMS), where our score of 90.3 surpasses the next-best competitor by over 6 points, showcasing superior human- like planning. These comprehensive results confirm model's ability to translate its predictive accuracy into robust, safe, and efficient real-world driving actions.
Table 3: Performance on the Closed-loop NAVSIM.
Methods
NCT
DACT
TTC1
Comfort
EP
PDMS
TransFuser (Chitta et al., 2022)
97.7
92.8
92.8
100
79.2
84.1
UniAD (Hu et al., 2023)
92.9
100
78.8
83.4
Para-Drive (Weng et al., 2024h)
97.9
92.4
93.0
99.8
79.3
84.0
AutoDríve-R2 7B
98.55
95.9
95.4
100
82.7
89.1
Visualization Fig. 2, we present a comparative analysis of our method against other approaches in the nuScenes dataset. Notably, Qwen2.5-VI.-7B fails to generate accurate predictions in specific scenarios (c.g., (b) and (d)), whereas EMMA+ exhibits significant trajectory deviation. In contrast, method consistently achieves more reliable and physically feasible trajectory planning under varying illumination environments and complex motion patterns.
8

---

Published as a conference paper at ICLR 2026
3.3 ABLATION STUDIES
Training Stages Drawing inspiration from DeepSeck-RI-Zero, we first attempt to train the model solely through RL. As shown in Tab. 4, the model purely trained on RL (7B + RL) underper- forms that of SFT (7B + SFT) by 22.2% in average L2 error. We attribute this to model's inability to establish structured reasoning chains, as RL struggles to explore the high-dimensional rea- soning space required for multi-stop calculations and contextual logic synthesis. This observation validates the necessity of our two-stage training.
Table 4: Ablation studies of trajectory L2 er- rors on nuScenes dataset for validation.
L. Error (m)
Method
1s
2s
38
Avg.
Qwen2.5-
0.46
1.33
2.55
1.45
Qwen2.5-VI.-7B- + SFT
0.17
0.27
0.36
0.27
Qwen2.5-VI--7B + RI.
0.21
0.33
0.44
0.33
SFT: w/o. Four.
0.19 0.25
0.32
0.25
SFT: w/o. Self.
0.17 0.23
0.29
0.23
Supervised Fine-tuning (SFT) In the first stage, the baseline Qwen2.5-VL-7B (7B) achieves an av- erage 1.2 error of 1.45m, whereas the SFT model (7B + SFT) trained on the nuScenesR2-6K dataset 81.4%
RL: w/o. Tpos
0.32 0.53 0.72
0.53
RL: w/o, Tae
0.14 0.20 0.27
0.21
RL: w/o. Tvel
0.15 0.21 0.29
0.22
RL: w/o. Tiem
0.15 0.23
0.34
0.24
reduces this to 0.27m, demonstrating improvement. This significant enhancement high- lights the effectiveness of adopting SFT training in establishing foundational reasoning capabilities. Moreover, removing four-step reasoning structure (w/o. Four.) increases the error to 0.25m, indicating a 31.5% degradation compared AutoDrive- R Similarly, eliminating self-reflection (w/o. Self.) results in 0.23m error, representing 21. 1.11% % decline relative to AutoDrive-R* This emphasizes the interdependence of both four-step logical chain and self-reflection mechanism in constructing high-quality CoT dataset.
AutoDrive-Re 7B
0.13 0.19 0.25 0.19
Learning (RL) In the second stage, we evaluate the contribution of individual re- ward components within the physics-groundee alignment is critical for maintaining global geometric path accuracy, as its removal (w/o. Tpox in- creases the error to 0.53m, much higher than the full model. Moreover, steering angle regulation ensures kinematic feasibility by penalizing abrupt changes in steering adjustments, and its absence (w/o. Tste) leads to 10.5% degradation (0.21m). Additionally, velocity consistency constraints en- sure adherence to target speed profiles by penalizing deviations in predicted velocity from ground- truth values, and their exclusion (w/o. Tvel) raises the error to 0.22m. Finally, temporal smoothness penalties suppress unstable control patterns by penalizing abrupt changes in steering and velocity across time steps, and their removal (w/o. fium) results 26.3% increase in error (0.24m). By tegrating all four components into our physics-groundee reward, AutoDrive-Re R2 achieves an optimal 0. 1.19 L2 error, confirming the necessity of each element in achieving reliable trajectory planning.
Reinforcement
framework of AutoDrive-R2. Specifically, spatial
Key Hyperparameters In Tab. 5, We conduct series of ablation studies to analyze the impact of key hyperparameters. First, for the reward weights (A), we find that uniform weights (A = (1, 1)) achieve a lower average L2 error (0.19 m) com- pared to decaying weights (0.22 m). This indi- cates that all reward components are equally im- portant for the learning process, leading us to adopt the uniform setting. Next, we analyze the KL-divergence coefficient 3. A value of B = 0.04 achieves the best performance with a 0.19 m error. A smaller value (8 = 0.02) results in a higher er- ror (0.21 m), while a larger value (3 = 0.06) also slightly degrades performance (0.20 m). likely by overly constraining the optimization. Finally, for the number of generations (G), we observe perfor- mance improving as G increases from 2 (0.23 m) to 6 (0.19 m). However, increasing G further to yields no additional benefit. Therefore, we set G = 6 to balance performance and computation.
Table 5: Ablations on key hyperparameters.
L2 Error (m)
Setting
1s
28
38
Avg.
Reward Weights (N)
A: 0..4.033.
0.15 0.22
0.29
0.22
A (1,1,,11 (Ours)
0.13 0.19 0.25
0.19
KL-divergence Beta (B)
8 = 0.02
0.14 0.21
0.27
0.21
8 0.04 (Ours)
0.13
0.19
0.25
0.19
B 0.06
0.13 0.20
0.26
0.20
Number of Generations (G)
G 2
0.16 0.23 0.31
0.23
G = 4
0.14
0.20
0.26
0.20
G 6 (Ours)
0.13
0.19
0.25
0.19
G 8
0.13 0.19
0.25
0.19

---

Published as a conference paper at ICLR 2026
4 RELATED WORK (EXTENDED VER. IN APPX. B)
Autonomous Driving The evolution of autonomous driving systems has transitioned from modular architectures to end-to-end learning paradigms. Early works like UniAD (Hu et al., 2023) pioncered the integration of sub-tasks into a unified framework. Concurrently, other methods addressed core challenges in prediction and efficiency; for example, Flash (Antonello et al., 2022) focused on cre- ating fast models for real-time application, while DiPA (Knittel et al., 2023) tackled the complex, multi-modal nature of agent interactions. These works highlighted the need for holistic evaluation beyond simple 1.2 error, inspiring our new analyses on collision rate and closed-loop performance. Subsequent methods like Para-Drive (Weng et al., 2024a) and BEV-Planner (Li et al., 2024) contin- ued to refine these integrated approaches.
The advent of vision-language models (VLMs) has further transformed the field. Dri (Tian et al., 2024) and DriveMLM (Wang et al., 2023) enabled systems to incorporate linguistic reason- ing for decision-making and rationale generation. Alongside performance, explainability has be- come a critical concern. A systematic review in (Kuznietsov et al., 2024) provides comprehensive overview of techniques for building trustworthy systems, while works like Interpretable Goal-based Prediction and Planning (Albrecht et al., 2021) explored generating explanations by making the model's goals explicit. In contrast to these post-hoc or explicit-goae methods, our AutoDrive-Ra introduces novel form of intrinsic explainability, representing a significant step towards inherently trustworthy AI for autonomous driving.
General VLMs The development of general vision-language models (VLMs) has been driven by the success of large language models (LLMs) in understanding textual data. CLIP (Radford et al., 2021) established a foundational approach by aligning image and text features through contrastive learning, enabling zero-shot generalization. Building on this, BLIP (Li et al., 2022) and its successor BLIP- 2 (Li et al., 2023) refined multimodal alignment using contrastive and matching losses to improve contextual grounding. More recent models, such as LLaVA (Liu et al., 2023) and Qwen2.5VL (Bai et al., 2025), have integrated robust LLMs with vision encoders to enhance representation capa- bilities. The OmniGen2 (Wu et al., 2025) framework further advanced this by introducing dual decoding pathways for text and image generation, while DeepSeek- V3 (Liu et al., 2025) demon- strated efficient inference through Mfxture-of- (MoE) architecture with auxiliary- loss-free load balancing. These advancements highlight the growing synergy between vision and language modalities in multimodal learning.
Reinforcement Learning for Fost-Training Reinforcement learning (RL) has emerged as a criti- cal tool for refining model capabilities post-training. Techniques like Proximal Policy Optimiza- tion (PPO) (Schulman et al., 2017) have been instrumental in fine-tuning models for complex tasks, as seen in the optimization of GPT (Achiam et al., 2024). Direct Preference Optimiza- tion (DPO) (Rafailov et al., 2023) introduces a sampling-free free parameterization for reward mod- els, streamlining the fine-tuning process. Reward Fine-Tuning (RFT) (Yuan et al., 2023) has shown particular efficacy in mathematical reasoning, while Guided Reward Policy Optimization (GRPO) (Shao et al., 2024b) enables robust reasoning improvements without external toolkits. The DeepSeek-RI (Guo et al., 2025) model exemplifies the application of GRPO, achieving state-of- the-art results through physics-informed reward design. These methodologies underscore the im- portance of RL in aligning model outputs with real-world constraints and user preferences.
5 CONCLUSION
In this work, we propose AutoDrive-Ro R2, a novel VLA framework designed for reasoning-guide tra- jectory planning in autonomous driving. AutoDrive R² effectively balances semantic understanding with real-world constraints through two-stage training framework: (1) SFT stage adopting the nuScenesR226K dataset, which employs a four-step CoT process to cultivate structured reasoning and self-reflection for validation, and (2) a RL stage leveraging GRPO training refine trajectory planning under physics-groundee rewards. Experiments validate the efffcttivnness of AutoDrive-R? achieving SOTA performance on nuScenes (34.5% reduction vs. EMMA+) and Waymo (90.7% reduction vs. Qwen2.5-VLL-7B) demonstrating strong zero-shot generalization. Future efforts will focus on multi-agent coordination and real-time sensor fusion integration to further improve adapt- ability in complicated environments.
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
Our research builds upon publicly available autonomous driving datasets, including nuScenes and Waymo, construct the nuScenesR--6K dataset. The manual annotation process focuses exclu- sively on labeling contextual reasoning chains and trajectory planning scenarios, ensuring no per- sonally identifiable information (PII) is collected or inferred. The dataset is fully anonymized, em- phasizing high-level environmental and traffic dynamics while safeguarding individual privacy. We advocate for the responsible application of AutoDrive-R?, urging stakeholders to prioritize safety, fairness, and transparency in deployment. Specifically, we caution against misuse in surveillance or discriminatory decision-makingg particularly in scenarios where autonomous systems could dispro- portionately impact vulnerable populations. All experiments adhere to rigorous academic standards, with a commitment to open science: we publicly share our dataset, code, and training protocols to enable independent verification, ethical serutiny, and collaborative refinement. By fostering trans- parency and community engagement, we aim to ensure that advancements in autonomous driving align with societal values and regulatory frameworks.
REPRODUCIBILITY STATEMENT
To ensure the full reproducibility of our findings, have provided detailed implementation and methodological descriptions throughout the paper. The construction the nuScenesR2-6K dataset, including its 6,000 manually annotated image-trajectory pairs and statistical properties, is described in Sec. 2.1 and Appendix D. The AutoDrive-R' framework, with its four-step reasoning process (Observation + Calculation + Logic + Reflection) and self-reflection validation mechanism, is thoroughly outlined in Sec. 2.2, where we also provide the structured input prompts used to generate chain-of-thour (CoT) data. Key components of the Group Relative Policy Optimization (GRPO) training algorithm, including the -ersuss-rrounee reward function, spatial alignment, vehicle dy- namics, and temporal smoothness criteria, are detailed Sec. 2.3 and Appendix C. To further facilitate replication, will publicly release the nuScenesR2-61 dataset, pre-trained model check- points, and training code upon acceptance, adhering to open scientific principles. Additionally, all hy typerparameeers, evaluation protocols, and ablation study configurations are explicitly documented in Appendix A, ensuring transparent and rigorous experimental validation.
11

---

Published as a conference paper at ICLR 2026
REFERENCES
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Ale- man, Diogo Almeida, Janko Altenschmidt, and al. Gpt-4 technical report, 2024. URL
https: / /aaxiiiv... org/ atg/abs////eeao..ooe//g/g////aer /2003 08774.
Stefano V Albrecht, Cillian Brewitt, John Wilhelm, Balint Gyevnar, Francisco Eiras, Mihai Do- bre, and Subramanian Ramamoorthy. Interpretable goal-based prediction and planning for au- tonomous driving. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. (044-1049) IEEE, 2021.
Morris Antonello, Mihai Dobre, Stefano V Albrecht, John Redford, and Subramanian Ramamoor- thy. Flash: Fast and light motion prediction for autonomous driving with bayesian inverse plan- ning and learned motion profiles. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9829-9836. IEEE, 2022.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang. Wenbin Ge. Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang. Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report. arXiv preprint ar arxiv:2502. 10.000..3992 13223, 2025.
Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubezak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, James Tanner, Quan Vuong, Anna Walling, Haohuan Wang, and Ury Zhilinsky. No. A vision- language-action flow model for general robot control, 2024. URL htttps://arxiv.ort 00//2211..21111
Tom B. Brown, Benjamin Mann, Nick Ryder. Mclanie Subbiah, Jared Kaplan, Prafulla Dhari- wal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners, 2020. URL https: // arxiv. org/ abss 2005. 14165.
Long Chen, Lukas Platinsky, Stefanie Speichert, Blažej Osiński, Oliver Scheel, Yawei Ye, Hugo Grimmett, Luca Del Pero, and Peter Ondruska. What data do we need for training an av motion planner? In 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 1066- 1072. IEEE, 2021.
Rui Chen, Lei Sun, Jing Tang, Geng Li, and Xiangxiang Chu. Finger: Content aware fine-grained evaluation with reasoning for ai- ti-generated videos. arXiv preprint arXiv:2504.10352 2025.
Shaoyu Chen, Bo Jiang, Hao Gao, Bencheng Liao, Qing Xu, Qian Zhang, Chang Huang, Wenyu Liu, and Xinggang Wang. Vadv2: End-to-end vectorized autonomous driving via probabilistic planning, 2024. URL https:tt//// //appii//axxv.../// wwwpr/wwwwwg/.b//////rr.rb 220.///.b/2222222////
Kashyap Chitta, Aditya Prakash, Bernhard Jaeger, Zehao Yu, Katrin Renz, and Andreas Geiger. Transfuser: Imitation with transformer-basee sensor fusion for autonomous driving. IEEE trans- actions on pattern analysis and machine intelligence, 45(11):12878-122) 12895, 2022.
Daya Guo, Dejian Yang, Haowei Zhang. Junxiao Song. Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, and et al. Deepseck-rl: Incentivizing reasoning capability in llms via reinforcement learning, 2025. URL htt.h:ttt///eet http:///aavvvvv. org.
Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tian- wei Lin, Wenhai Wang, Lewei Lu, Xiaosong Jia, Qiang Liu, Jifeng Dai, Yu Qiao, and Hongyang Li. oriented autonomous driving. In 2023 IEEE/CVF Conference Computer Vi- sion and Pattern Recognition (CVPR), pp. 17853-17861 2023, doi: 10.1109/CVPR522292202.. 01712.
12

---

Published as a conference paper at ICLR 2026
Zhijian Huang, Chengjian Fen, Feng Yan, Baihui Xiao, Zequn Jie, Yujie Zhong, Xiaodan Liang, and Lin Ma. Drivemm: All-in-one large multimodal model for autonomous driving. arXiv preprint arXiv:2412.07689, 2024.
Jyh-Jing Hwang, Runsheng Xu, Hubert Lin, Wei-Chih Hung, Jingwei Ji, Kristy Choi, Di Huang, Tong He, Paul Covington, Benjamin Sapp. al. Emma: End-to-end multimodal model for au- tonomous driving. arXiv preprint arXiv:2410.23262, 2024.
Bo Jiang. Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, and Xinggang Wang. Vad: Vectorized scene representation for efficient au- tonomous driving. 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 8306-8316, 2023,
doi: 10.1 10.10091CCVV110000222.0000
Bo Jiang, Shaoyu Chen, Qian Zhang. Wenyu Liu, and Xinggang Wang. Alphadrive: Unleashing the power of vims in autonomous driving via reinforcement learning and reasoning, 2025. URL 07608.
https://aaae http///axvvvvv.///vv///ee/ea.av/
http:///awwxwx.....aa./ org htcp:/bs/22555
Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, and Amar Shah. Learning to drive in day. In 2019 International Conference on Robotics and Automation (ICRA). pp. 8248 8254. IEEE. 2019.
Anthony Knittel, Majd Hawasly, Stefano V Albrecht, John Redford, and Subramanian Ramamoor- thy. Dipa: probabilistic multi-modal interactive prediction for autonomous driving. IEEE Robotics and Automation Letters, 888):4887-4894, 2023.
Anton Kuznietsov, Balint Gyevnar, Cheng Wang, Steven Peters, and Stefano V Albrecht. Explain- able ai for safe and trustworthy autonomous driving: A systematic review. IEEE Transactions on Intelligent Transportation Systems, 2024.
Nathan Lambert. Reinforcement learning from human feedback, 2025. URL https:
org/ arg/abs// aba.///4..... 2504. 1144..2555 12501.
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In Kamalika Chaud- huri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (eds.), Pro- ceedings of the 39th International Conference Machine Learning, volume 162 of Proceed- ings of Machine Learning Research, pp. 12888 129000 PMLR, 17-23 Jul 2022. URL https: / //proceedings ml www.mw...ssss..... wwwppprsssvvll.. 62 1222 html.
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. BLIP-2: Bootstrapping language-image pre- pre-training with frozen image encoders and large language models. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (eds.), Proceedings of the 40th International Conference on Machine Learning, volume 202 of Pro- ceedings of Machine Learning Research, pp. 19730-19742, PMLR, 23-29 Jul 2023. URL. https: /apps:/ppaee http:///poceeieeeeeeeeeo m.....mr.pppp www.m.rr.rrr...mmmmmmmmmmmmmmere v2 123q html.
Zhiqi Li, Zhiding Yu, Shiyi Lan, Jiahan Li, Jan Kautz, Tong Lu, and Jose M. Alvarez. Is ego status all you need for open-loop end-to-end autonomous driving? In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 14864- 14873, June 2024.
Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, and al. Deepseck-v3 technical report, 2025. URL https:
//aaxiv.org//bbabb....aa /2les.241...2 19437.19..
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. In NeurIPS, 2023.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agar- wal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision, 2021. URL http:////ewvviv..//////////// 00020.
13

---

Published as a conference paper at ICLR 2026
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36:53728-5533 -37411, 2023.
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms, 2017. URL https: /ttrppp://rxii...o wwwgg//b//ai 1707.06347.
Hao Shao, Yuxuan Hu, Letian Wang, Guanglu Song, Steven L.. Waslander, Yu Liu, and Hongsheng Li. Lmdrive: Closed-looy end-to-end driving with large language models. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15120-15130, 2024a. doi: N..1099CVPR55233..22200000
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing limits mathematical reasoning in open language models. arXiv preprint arXiv:2402.08:: 03300, 2024b.
Wenchao Sun, Xuewu Lin, Yining Shi, Chuang Zhang, Haoran Wu, and Sifa Zheng. Sparsedrive: End-to-end autonomous driving via sparse scene representation, 2024. URL hwtph:ttt///ettt //axiv.. org/abs 2405. 19620.
Xiaoyu Tian, Junru Gu, Bailin Li, Yicheng Liu, Yang Wang, Zhiyong Zhao, Kun Zhan, Peng Jia, Xianpeng Lang, and Hang Zhao. Drivevlm: The convergence of autonomous driving and large vision-language models, 2024. URL https: arxiv, wwwxiivooz org/ /bs.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Ar- mand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation
language models, 2023. URL https: //txxvvvvvv/////////////////aav/ .ww.voorrr/eeeee awwg///b////a /tt.s//223..22 13971.
Shihao Wang, Zhiding Yu, Xiaohui Jiang, Shiyi Lan, Min Shi, Nadine Chang, Jan Kautz, Ying Li, and Jose M Alvarez. Omnidrive: A holistic Ilm-agent framework for autonomous driving with 3d perception, reasoning and planning. CoRR, 2024.
Wenhai Wang. Jiangwei Xie, Chuan Yang Hu, Haoming Zou, Jianan Fan, Wenwen Tong, Yang Wen, Silei Wu, Hanming Deng, Zhiqi Li, Hao Tian, Lewei Lu, Xizhou Zhu, Xiaogang Wang, Yu Qiao, and Jifeng Dai. Drivemlm: Aligning multi-modal large language models with behavioral planning states for autonomous driving, 2023. URL https: :eatts::/arxee //tpaaaaivv////////////a////awaa org/ abs 22t...21.... 09245.
Xinshuo Weng, Boris Ivanovic, Yan Wang, Yue Wang, and Marco Pavone. Para-drive: Parallelized architecture for real-time autonomous driving. In 2024 IEEF/CVF Conference Computer Vi- sion and Pattern Recognition (CVPR), pp. 15449-15458, 2024a. doi: 10.100999VP25223.22224 01463.
Xinshuo Weng, Boris Ivanovic, Yan Wang, Yue Wang, and Marco Pavone. Para-drive: Parallelized architecture for real-time autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15449-15458, 2024b.
Chenyuan Wu, Pengfei Zheng, Ruiran Yan, Shitao Xiao, Xin Luo. Yueze Wang, Wanli Li, Xiyan Jiang, Yexin Liu, Junjie Zhou, Ze Liu, Ziyi Xia, Chaofan Li, Haoge Deng, Jiahao Wang, Kun Luo, Bo Zhang, Defu Lian, Xinlong Wang, Zhongyuan Wang, Tiejun Huang, and Zheng Liu. Omnigen2: Exploration to advanced multimodal generation, 2025. URL org/abs /206 18871.
Zhenhua Xu, Yujia Zhang, Enze Xie, Zhen Zhao, Yong Guo, Kwan-Yee K Wong, Zhenguo Li, and Hengshuang Zhao Drivegpt4: Interpretable end-to-end autonomous driving via large language model. IEEE Robotics and Automation Letters, 2024.
Zhenjie Yang, Yilin Chai, Xiaosong Jia, Qifeng Li. Yuqian Shao, Xuekai Zhu, Haisheng Su, and Junchi Yan. Drivemoe: Mixture-of-experts for vision- sasiio-language-acttoo model in end-to-end au- tonomous driving, 2025. http:t /ttp///aavv//////////////////avl wwwp..owog/bb//55000000 16788.
14

---

Published as a conference paper at ICLR 2026
Tengju Ye, Wei Jing, Chunyong Hu, Shikun Huang, Lingping Gao, Fangzhen Li, Jingke Wang, Ke Guo, Wencong Xiao, Weibo Mao, Hang Zheng, Kun Li, Junbo Chen. and Kaicheng Yu. Fusionad: Multi-modalig, fusion for prediction and planning tasks of autonomous driving, 2023.
URL https:tpp///ccm /estps://rriinn /wtxiv.vvvvoooo/ orgg abs/ 01006.
Gokul Yenduri, Ramalingam M. Chemmalar Selvi G, Supriya Y, Gautam Srivastava, Praveen Ku- mar Reddy Maddikunta, Deepti Raj G, Rutvij H Jhaveri, Prabadevi B, Weizheng Wang, Athana- sios V. Vasilakos. and Thippa Reddy Gadekallu. Generative pre-trained transformer: A compre- hensive review on enabling technologies, potential applications, emerging challenges, and future directions, 2023. URL https: / rxxiv. orgoor///aaee aabs/bb////e 2305 1...3.00000 0435.
Zheng Yuan, Hongyi Yuan, Chengpeng Li, Guanting Dong, Keming Lu, Chuanqi Tan, Chang Zhou, and Jingren Zhou. Scaling relationship on learning mathematical reasoning with large language models, 2023. URL https:/ /
rxiv. orgg abs 2208. 01 825.
Jiang-Tian Zhai, Ze Feng, Jinhao Du, Yongqiang Mao, Jiang-Jiang Liu, Zichang Tan, Yifu Zhang, Xiaoqing Ye, and Jingdong Wang. Rethinking the open-loop evaluation of end-to-end autonomous driving in nuscenes, 2023, URL httph:: /atpiii///axv..////////sp org/ abs 2305. 10430.
Xingcheng Zhou, Xuyuan Han, Feng Yang, Yunpu Ma, and Alois C. Knoll. Opendrivevla: Towards end-to-end autonomous driving with large vision language action model, 2025a. URL https:
abs 2343..
Zewei Zhou, Tianhui Cai, Seth Z. Zhao, Yun Zhang, Zhiyu Huang, Bolei Zhou, and Jiaqi Ma. Au- tovla: A vision-language-attinn model for end-to-end autonomous driving with adaptive reason- ing and reinforcement fine-tuning, 2025b. URL htwph:ttt///t http///axvvvvvva////////e/eaaata www.vv.rggabba.... 2506 13757.
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny, Minigpt-4: En- hancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304. 10592, 2023.
15

---

Published as a conference paper at ICLR 2026
APPENDIX
In this appendix, we provide more details, related work, tool library, and discussions for a compre- hensive evaluation and understanding of our method. Detailed contents are as follows:
Experiment
16
A.1 More Hyperparameter Configurations
16
A.2 More Visualization Results
16
A.3 "Aha Moment"
16
Related Work
18
B.1
Autonomous Driving
18
B.2 General VLMs
18
B.3
Reinforcement Learning for Post-Training
19
C Vehicle Kinematics in Physics-Grounded Rewards
19
D Detailed Prompts to Generate CoT Data
20
E LLM clarification
21
A EXPERIMENT
A.1 MORE HYPERPARAMETER CONFIGURATIONS
Our method implemented on machine with an Intel(R) Xeon(R) Platinum 8480+ and eight NVIDIA H20 GPUs with 90G memory. The training process ran for 750 epochs without freezing the vision transformer (ViT) backbone, and the number of generation is set to 6 in GRPO algorithm.
A.2 MORE VISUALIZATION RESULTS
Fig. 3 provides additional visualization results in trajectory planning tasks between our AutoDrive- R and other methods on the nuScenes dataset. Notably, our method consistently outperforms other approaches in predicting both reliable and physically-feasibe trajectories. demonstrating the state- of-the-art performance of our proposed method.
To further illustrate the advantages of our structured reasoning process, present two representa- tive comparisons in Fig. and Fig. These visualizations explicitly contrast the four-stage CoT reasoning (AutoDrive-Pe with the single-step reasoning of Qwen2.5-VL-7B. As can be seen, the Qwen2.5-VL model predicts trajectory that deviates from the lane marking due to its simplified reasoning approach. The model's single-stage analysis fails account for the vehicle's kinematic constraints and results in an unrealistic leftward drift. contrast, AutoDrive-R** four-stage pro- cess systematically validates its predictions. These examples demonstrate how structured CoT framework enables systematic error detection and correction, resulting in trajectories that are both geometrically accurate and physically feasible. The integration of physics-grounder rewards in the GRPO stage further ensures these corrections align with real-world driving constraints.
A.3 "AHA MOMENT"
A compelling insight observed during the development of AutoDrive-Pe -R is the emergence of "rea- soning self-correction moment", where the model systematically identifies and resolves contradic- tions in its initial trajectory planning.
This reasoning celf-correction moment demonstrates AutoDrive-R'ss ability to re-examine its own assumptions and refine predictions through structured self-validation. As shown in Fig. 5, similar
16

---

Published as a conference paper at ICLR 2026
Quoed
Ambriiww
60
Figure 3: Qualitative comparison of trajectory planning performance across Qwen2.5-VL-78 EMMA+, and our AutoDrive-Re R2 on the nuScenes dataset. Note that blue lines denote predicted trajectories while green lines represent ground truth trajectories.
"hape" Vou ore autenenous drivig agent You haw acpsst a frost siew conero iarge of whhicle diago wrypsints
Tepart Image:
teeeetess glvn
oehcla's inferned from the images"
"information" "Pre ded ane the prer ous ege vehick stetss recorded over the lut 30 seconda (et 05 second (therdds) This includes the
ceandiotes
vchicke Rostive moro 000, Accelentiinn lift
dresi
006 0.12 mis' 2. Vilccity 5 m/s Sheerin angle 0.19
mile Veletty mie Cheerig orgin gii tum (1-2.0 2.50] I left negetien
5.93, Acceloominn -013
left ham.
negether
0.03 Wasenty xoos v 13 m/ll2, vesserty 4,25 mil Ohhering ongle 4 (ase the left ham negetiie rign sure, (t- ccs 40, a.01 Acco
Stwering
13
(1- bri
m/r' Wesothy mel Steenig angle (posiiiee left segetive: mlaa
Accelentionn
(peai he left nogilie right tand o bi (0.0. 0.0 Accelention 0.92. y 0.2 w'' Velccity: 3.03
w// Steering engle 0.06 lesetive left ham megeive right Turel"
AutoDrive.R
(uttrriee ar, oileled Vasal Archoss Vingge anasd realts Vehidees intended drectivn The véhille spocars to be moving stroight choad, as thane ane no viable aiges Troffic right
The dighty
ng
thothe
merione
controled acosss but trefric light
(aaud mag
Mottor thode rige U'inng hane dets whet vence Chen 7 tiee parte to posi 37 yo-0.0)) ets Im/e, arto -1 dizeeeee to bes4 03, yob D2) und Tirsis
0.1.
0000 y000 3.00// 0292
4_00
0.3
037
(.1))) 0.5 o.prteeee Postnce mnediction whh-1- O- 86 U1 (40 253- (1 b1" 11300 whill O- (4 (15)) x 05 D. x (-015) (0 11 -000m LLttrral effest by -
(012 0.29
0.0
2.86e/5
texill) - toetl 06) e titee forward
Legial beduction Seatsty check folliang rojertory, the vet
Ran ligh? no, on there
light
Centinue
Asome check cred pesition (x- 39, y- O roqures unarape spe speed of upeesinnllly 2 Biaa this scheenblee with vehicl's acceleaation hatury yes the vchile had been decelerating and the prodic speed precios sescities Adjas predicted
speed Vami Salf Reflection fon Validciinn 1oV
the
the fectille Mosdfy
soressi cheppin dreecll sheed blocking the velcctter Walt sped that there
vehicle decelennting
estine lnse: the vehicle conect poretbyy ron around it by contrning forward presouuty casumed Oh. I tee the entre grenine of moving forword flewed becouse the read impassable Lat's
down venicee mast come 18 complete stop before reaching bernee conid Aecolontion from
prad ched wayps
0.92
(n/s"), the vehicle can schie full stop sximatllyy 1.5 secends. The predicted to rest cleer to its stort mg pesition while enturig byy stfely and confor. vesseen)0 45, 6011 1085. 023 10.70 0.09 70,0 0.03, 10700 0.03. 1070. 0.03 vlndwwerr
should therefore reflect mis fesher aed decekrratio bringng the sehicle
Figure 4: "Aha Moment" of our AutoDrive- R2 on the nuScenes dataset.
"haal"
imager Your badk is to best predic future wopents for the clelee over the non fineilups, gvee the vih a intert enfered from the imses." 'irformation":
age
rxcond intervele) This incidde
conndiatte of
Postive
forword dinection
whils pos maree lnftwands The cano presanted is the fornat (s yi th- oo) (-14 80. -2 -2.0) Accelarsiiee
Webotty m/s
rngle -10 gesemee
fum negitiue right
evs'2 libuoiyy (pasl vil reft fun nagetine right fum)) 2.06) 105. 124 Accelention 0.2. y 0.35 m/s 2 Vescity 4.84 rus Steoring ande 3.09 Goos foo left ham, magiling right tum) (1 1.5 7.99. 0.7) Acceloviian Velertty 5.5 m/s Sheen
eegehin p4 (pontive left negettie right tum) (r-d bi) (-2.3 0.0 accelention x 1077 -d min't We cnty 5e wire Srearing andie: tose fum negettee rigst timi Do) 0, a.l Accaleratun
Veloomyy
(1-3 Del
(-5 15, -021) Accalenthonn -059. 402
-059 m/t's.
Autobrive-R
AutsDrnee
onalysts
fonce inft-no wable shrgght-thrugh opereeching nighworrd carve
forwerd woth blocked shapp enbenkment
exiets The aetace
clscr, but
ternon fances step er fure Obetocis chead A lange ippeesble con clepe occupine the left and carnnn fonword fieid seting a physioa serner straghtiine trave. orange
Traffic coner Mose
near the cave
contraled crea mearing castion pedestmane or atter
are gresent
"PPus" Vih de St with N- time paint Do. 18 86 2.8l 3.80is a- 10.25 0.64tess is p-- 13.8. 1577. will S0eels o- (0.17 0.30)//s to Ex000 y-0.0.
w-S Erte'ss oft Li6, o' Colollttios Average occeerssion ong x- (116 1.37 0.5 0.2 0.2 0.7 0.25) - 0.33' cngyy - (0.0 0.40 0.20 0.5 vedserty
v.y
continued ter(t) 5 54 1as (55) in -a the" wall mi Logial th "Safeyy check follanng this maaeehhry. will the whide: Ray red iiht? no (ne traffic rigna pedectrion crasnng?
af-1) a b2 CS. 05 (3)))) (0.5) x 2.72m y(h)) (40055)
3.5 (10 (0.5) -- 240 Lotural offach
dshected)" colida
shead? (as deteched)
pedestroza deteched) condustoon Decelencte gradually
within
with the dbstracted forward view Let me 1han the while'ss ouvent velccity (5 64ee') and regative
It's already slawnng but stopping withis 35 exeriing the right edge of the frome and nofice the continuuss white lene line ourves
requines sustuined deceloration of Dny's feaslee goon listory Welt
smouthly rightwerd beyond nerrowns, not dosure Lat's break
sbruptly.
contenunn brake exi nodiidee tepography.
gentle night along
The and
sofe inbophinal
reduced
speed (rd Un/s) whis treckong the lone centorine me both phys ooly coamble and cona sheat with infoastrcctur cues Self vdlidationn cort rns that turning right awoids
umvoeesrry stopping martars menestun alina the reads denge yodlrng revied woysamt aequence that follons carvetura.
Fredchap"*
Bases
wasports
for the veiccee over the eext 3 secands ane vnnoeen/2. 7.7. a 225 (9.3. 0550 14.6 (98), 45. 2.80) (199) 3372) 12.00 4 335p/oneeer
Figure 5: "Aha Moment" of our AutoDrive-R2 on the nuScenes dataset.
17

---

Published as a conference paper at ICLR 2026
behavior is observed during trajectory planning tasks: the model exhibits emergent capacity to detect inconsistencies in its motion modeling and resolve them via iterative recalculations.
Let me think, the vehicle's current velocity (5.64m/s) and negative acceleration
suggest it's already slowing, but stopping within 3s requires sustained decelera- tion of 1.0m/s3, feasible given history.
Wait, I' I'm re-examining the right edge of the frame and notice the continuous white lane line curves smoothly rightward beyond 5m, not ending abruptly: Oh, see, the embankment isn't 't a dead end; it's roadside topography, and the road itself continues along the curve. The cones and sign mark narrowing, not closure.
This autonomous reasoning process, characterized by four-stage self-reflection, is a hallmark of AutoDrive-RR's training pipeline. The integration of Shyyicc--grouddee rewards in the GRPO stage ensures that even minor corrections (e.g., adjusting lateral offsets or deceleration rates) are validated against roal-world constraints.
B RELATED WORK
B.1 AUTONOMOUS DRIVING
In recent years, autonomous driving has evolved from traditional modular detection, online mapping, prediction, and planning-toward end-to-end dearningbbssed ap- proaches (Hu et al., 2023; Jiang et al., 2023; Sun et al., 2024). UniAD (Hu et al., 2023) was the first integrate all sub-tasks into a cascaded model, achieving significant improvements over tra- ditional modular approaches. Some methods (Jiang et al., 2023; Ye et al., 2023; Chen et al., 2024) extract bird's s-eye view features and predict planning trajectories via multiple stages of interaction modeling. Para-Drive (Weng et al., 2024a) explores the design space of modular end-to-end au- tonomous driving stacks to significantly enhance both accuracy and inference speed. Additionally, Ego-MLP and BEV-Planner (Zhai et al., 2023; Li et al., 2024) investigate the role of ego-vehicle state and empirically validate influence through experimental results.
With the emergence of vision-languag models (VLMs), researchers have increasingly integrated both large language models (LLMs) and VLMs into autonomous driving to enhance overall sys- tem performance. Several approaches (Xu et al., 2024; Shao et al., 2024a) incorporate pretrained LLMs to generate driving actions along with interpretable textual explanations. Furthermore, DLi (Tian et al., 2024) incorporates specialized reasoning modules to improve situational understanding, while DriveMM (Huang et al., 2024) processes multi-view video and image inputs to enhance generalization in vehicle control. DriveMLM (Wang al., 2023) introduces behavior planning module to generate optimal driving decisions with accompanying rationales.
Moreover, the recent success of lision-lagguage-attion (VLA) models in robotics offers new per- spective for autonomous driving. DriveMoE (Yang et al., 2025) builds on the embodied Al frame- work 70 (Black al., 2024) and introduces Action-MoE by training routing networks to activate pert modules for diverse driving behaviors. Furthermore, OpenDrive VLA (Zhou et al., 2025a) pro- poses an agent-environment--e interaction model for precise trajectory planning. AutoVLA (Zhou et al., 2025b) directly predicts semantic reasoning and trajectory plans from visual inputs and lan- guage prompts.
B.2 GENERAL VLMS
In recent years, the success of large language models (LLMs) (Yenduri et al., 2023; Brown et al., 2020; Touvron al., 2023) has motivated researchers to extend them into vision-language models (VLMs) (Radford et al., 2021; Zhu et al., 2023), which integrate textual and visual data for richer multimodal representation. CLIP (Radford et al., 2021), a pioneering work, combines image and text features using an image encoder and text encoder to predict correct pairings of image-text examples via a zero-shot learning strategy. Similarly, BLIP (Li et al., 2022) and BLIP-2 (Li et al., 2023) are trained using an image-text contrastive (ITC) loss to align vision and language represen- tations, along with an image-text matching (ITM) loss to distinguish between positive and nega- tive image-text pairs, thereby enhancing visual representation grounded in textual context. Inspired
18

---

Published as a conference paper at ICLR 2026
by these methods, many VLMs such as LLaVA (Liu et al., 2023) and Qwen2.5VL (Bai et al., 2025) further enhance the robustness and representation capabilities of pretrained vision encoders by integrating large language model as the text encoder (e.g.. LLaMA (Touvron et al., 2023)). OmniGen2 (Wu et al., 2025) represents another notable VI.M, employing two distinct decoding pathways for text and image modalities with unshared parameters and decoupled image tokenizer. Notably, DeepSeek-V3 V3 (Liu et al., 2025) introduces a robust Mixture-of-Experts (MoE) language model that employs an auxiliary-loss-iit strategy for load balancing, achieving both efficient and cost-effective inference.
B.3 REINFORCEMENT LEARNING FOR POST-TRAINING
Reinforcement learning (RL) has been widely adopted in large language models, and researchers have found that reinforcement learning from human feedback (RLHF) (Lambert, 2025) can signif- icantly enhance their reasoning capabilities. Among these methods, Proximal Policy Optimization (PPO) (Schulman et al., 2017) was initially used in simulated robotic locomotion and Atari game environments, and later employed by OpenAI to fine-tune GPT (Achiam et al., 2024), resulting in substantial improvements in text generation tasks. Unlike conventional RLHF methods, direct Preference Optimization (DPO) introduces a new reward model parameterization that eliminates the need for sampling during fine-tuning (Rafailov et al.. 2023). Reward Fine-Tuning (RFT) (Yuan et al., 2023) is another RL-based approach that demonstrates strong performance in mathematical reasoning tasks. Furthermore, Guided Reward Policy Optimization (GRPO) (Shao et al., 2024b) ef- fectively improves the reasoning capabilities of LLMs without relying on external toolkits or voting mechanisms. DeepSeek-RR RI (Guo al., 2025), for example, leverages GRPO to fine-tune its model and achieves superior performance compared to existing methods. Inspired by these approaches, recent work (Chen et al., 2025) adopts similar fine-tuning strategies to enhance the reasoning capa- bilities of language and multimodal models.
C VEHICLE KINEMATICS IN -RRUNDDD REWARDS
The physical constraints of autonomous driving systems are deeply rooted in vehicle kinematics and passenger comfort principles. Vehicle kinematics governs the relationship between steering geometry, tire friction, and accelcration limits, ensuring that predicted trajectories adhere to the physical capabilities of the vehicle. For instance, abrupt steering adjustments can violate the min- imum turning radius determined by the vehicle's wheelbase L and maximum steering angle 8na- The minimum turning radius Rmin is defined as
L
Rmin sin(fmaxx)
(9)
where is the distance between the front and rear axles (wheelbase), and &max is the maximum achievable steering angle of the front wheels. Any trajectory requiring a tighter turn than Rmin would be physically infeasible, leading to tire slippage or loss of control. Additionally, lateral acceleration ag during cornering must satisfy
2
0g
< pg,
(10)
R
where v is the vehicle speed. R is the turning radius, is the tire-road friction coefficient (typically Ja As 0.8), and g is gravitational acceleration (9.81 m/s²). Exceeding this threshold results in unsafe side-slip, particularly on low-friction surfaces like wet or icy roads. Beyond kinematic feasibility, passenger comfort critically tied to smooth motion dynamics. Sudden changes in acceleration, known as jerk jt), directly impact rider experience. Jerk is defined
da(!) j(l) = dt
(11)
where att) is the instantaneous acceleration. Human tolerance for jerk is generally below 2.5 m/s3. and abrupt steering or acceleration adjustments (e.g.. 0g 28--8pm f--1 or 2kk (k-1) can induce discomfort jerky motions. Furthermore, rapid maneuvers amplify vibrations in the vehicle suspension system, modeled as a second-order differential equation:
mă + ct + kx F(I)
(12)
19

---

Published as a conference paper at ICLR 2026
where is the sprung mass (mass supported by the suspension), is the damping coefficient, kt the spring stiffness, x is the vertical displacement the suspension, and F(t) is external forces (e.g., centrifugal force during sharp turns). Excessive F(t) due to abrupt motions overwhelms the suspension, increasing perceived jolts and reducing ride quality.
These principles directly addressed in the physics-grounded reward framework. By penalizing abrupt changes in steering angle and velocity through temporal smoothness terms Tiem- the method ensures that trajectories remain within the vehicle's kinematic limits while minimizing jerk and vibration. This approach aligns with the experimental validation in the main text, where removing Tiom led 26.3% increase trajectory error, underscoring the necessity of balancing geometric accuracy with physical and physiological constraints.
D DETAILED PROMPTS TO GENERATE COT DATA
During the supervised fine-tuning (SFT) stage of AutoDrive-R?, we designed structured input prompt to generate high-quality chain-of-thought (CoT) data for the nuScenesR2-6K datasets. The prompt template as follows:
#FF Prompt:
You are given image, a driving-related question, Generate a four-stage reasoning process with explicit mathematical
answer.
modeling and self-validation. Engage in an internal dialoque using expressions such as "let me think', 'wait', 'Hmm', 'oh, I see', 'let' break it down', etc, other natural language thought expressions. It's encouraged to include solf-refloction or verification in the reasoning process.
### Input Format:
System Instructions: (original_tase) Past Vehicle Status: pariginaa_informaaiinn
Preciction Task: Becliiina__prbbleee
Answer: toriginal_soluta
WFF Output Format:
NFF 1. Visual Analysis
"Image analysis results:
Vehicle's intended direction: Left turn (steering wheel angle: \theta rad)
Obstacles ahead:
* Car detected ahead (moving right/Left/straigiee
Feecssrian crossing road (left/right side)
Traffic signal: signal_statur detected (red green / yellow)
### 2. Motion Madeling
"Using ng historical data in Past Vehi cle Status with N time points:
h =1,9 = Vil, = vim/s, ay mm/s²
22 22, v2l. v2m/s. (22
in xn vn]. vn/a/s (ar,, : Gy /m// 5
Calculations:
Average acccleration:
Grang = 1 az J = arang 11//
agavs = E dw:)/N ay,ey m
Velocity prediction:
0, = Un azaug x 6, two + azang x 8z
fy U. Agang x 6 Vio Qyaup X 6z
Fosition prediction:
+(t+1)== + x & + 0.5 azang x 82
1( = b d. + 0.5 x Oyanp
x 8
lateral offset: 8y tan(0)
tan
20

---

Published as a conference paper at ICLR 2026
### 3. Logical Deductions
"Safety check:
If following this trajectory, will the vehicle:
Run a red light? + yes/no
Collide with car ahead? yes/no
Hit pedestrian crossing? yes/no
Conclusion: recommended_acte action (e.g., SSoop inmediately', 'Reduce speed to 5m/s")"
### 4. Self-Refiection Validation
"Valication
Assumption check:
Predicted position (x-x_pred, y-y_pred) requires average speed of v
/s
Is this speed achievable with vehicle's acceleration history? yes/ no
Adjustment:
If not fcasible + Modify trajectory by reducing speed or increasing stopping distance"
This structured prompt ensures nuScenesR2-6i dataset contains diverse and causally plausible rea- soning process, which are critical for cultivating the model's foundational perception and planning capabilities before RI. fine-tuning.
E LLM CLARIFICATION
We clarify the role of Large Language Models (LLMMs) in the preparation of this manuscript. Specifi- cally, LLMs were employed to refine the language, grammar, and overall readability of the text. This involved tasks such as correcting grammatical errors, improving sentence structure, and enhancing the flow of the narrative ensure clarity and precision in scientific communication. It is critical to emphasize that all core scientific contributions, including the formulation the research prob- lem, the development of the AutoDrive-RP R framework, the creation of the nuScenesR dataset, the design of the two-stage training methodology combining chain-of-thought reasoning and Group Relative Policy Optimization (GRPO) with physics-grounded rewards, and the comprehensive perimental analysis on nuScenes and Waymo benchmarks, are the result of the authors' independent work. The LLM was strictly utilized linguistic tool and did not contribute the conceptualiza- tion, implementation, or evaluation of the proposed methods. All intellectual property. including the theoretical foundations, algorithmic innovations, and empirical validations, originates solely from the human authors.
21

---

Published as a conference paper at ICLR 2026
"bask"; "Vou one auternnous driving ognar. You hose pccess io front predict furune
magao
your
the
gvan the whichh's e mott interned from the images. informatton": "Pravded are the prevous age viiice statue recorded includer
intevcle)) forword direcioon velvcle. Festhves
coerdinster of
Image:
whil peattiss y mooe lefhwords The deta in presented in the formot [x. yl Ext 29 0.04) Accelentton: 0.3. (244 2. Veloott. ongle
right (h-3.00) (000, 00) Acselentionn X 0.35. 0.59 mis'd, Valecity 6,53
m//s Sheanig angle 04 (pentive left turn regetive right humn"
(o) Q 5 VL (fall)
(b) AutsDrive.-2 (successs)
Quent 5-VL- 78: choolset think through this step byustep HMP I. Visual And *nVehille's Enhended Dircherf* The vehicle carmently hurning left, os ind cateed by the sheering ongle of 0.53 rodient (pontie for left tum)- inchstodles abeogth; There are care chead, but they appear to be moving the some direchen the ege vehicle, he pedestrans one bib cressing the
Vehcle Statur" The napell motely 6.53 ry'a 0.53 m/s"" 0.5 5.50 m/s, and the y-dinechon will oppooxmaatlyy 6.53 ma/s 0.59 m'a" Q.5 a 6 n/s. "*Posshon on Fred chiorhh The faaitiee i the x-draction will be y nubdie
troffic Signgnn traffic light green, along the vchicle preceed Hua
on Modeling
appresmattly 3.5 wis "ylelo Presichin ont Using the onerage accelerator, the velccity the x-dinesh on will
2) can ahead The pedestrize creasnng not vaiale, there rish bitting pedestrias "Saecaeeerded Ac Aetiath, The vehicle should continue famiing left at the curnent and trajectery 2822 Self fection Valiarion Assumption Checken The predicted pes the (e 3-4E 3.54 n) fezs bl with the curnent accelrstion history. "Adjummmmr No adjuetentt needed the trajectory safa and acheoablee Giwn the andyeie the whicle will continee turning left and preceed straight aneed </thiloo conseers
(0.5
the didrectionn traffic
sppres 0.0 6.77
0.5
chezken
rurning
[3.4 3.54. [
AutsDriwe-R i cra sinut onalynet V'ingg ondyste resulte vesicle i intarded disccioo left sym (ensoring whed angle e 54 rec) -chttecine ohoad. car detected aheed (rowing straight)
pedestrion creasing reed traffe aggnl: green light denected (egnal metust greas)" and notice medelegtt Viengg
yrbo) 55 0.315 05 6.56m/s way- 0 0.55 0.5 265m// is poomion prodiction affec] 6.50 055 0.5 G.15 (0.5)" 3.3 v(hi) D 0 255 055 65 D55 - (0.5) a alon - -lstenal offeet: dy 6 hanl 54) for 2g0 wis by anductiona lefftty chade fullwinn tratectory will mantomed)
(en- (047 0599/2 w 0.50m/s 0.47
accelengton: moguur (0.3 03332 e 315t5eee
prediction
mun red light? no (rraf signa it green
peprdertrion croming?
posthon (and 23. recommended oction
ABG
seff-reflechan teldetee* VVdlisshion scceleration hatorys yea (veticle has been sccelenttnn steacly) adjammennt of not fecaible modity trejechory by reducing speed or moreessng stopp ng distance -hh sex, vehcce clear path with no mediatt obsfacies so prodicted trajectory seams 'eeased
check
spord 5 5.50m/s -i this speed och eobble with vehvcle's
break down forther next steme and and predection 0.38] [15.12, 0.75),
0.05 (B 33, 0.08]. (6.79, 0. 0.16). 19.59. 0.38). 0.75). [16. 65, 1 1.22) [19 1.88)./erwwer!
98, 168) ufthoio
Figure 6: Visualization comparison between Qwen2.5- -V--BB and AutoDrive-Re R2 on nuScenes.
"les"" YYou are autunamous driein opent You have access a Tren" How corsera magg a vahicle vmager Your task de your best timesteps given
predict weypoints
vehclle
vohicle's intent
"iffer nottinn:: Prewized are the previcus ego vehicle statue recor ded over the lcst 3. secerds (ot 0.5 secord interoady This includes the ogo
Input Image:
forword droctior (1-3 (60 1-17 a5 0.94. Aooe anetian K.I.l y 0.25 m/a"2 Velacity 5.92 m/s Steenng cnclel 0.99 (peathee left fur repetive right hum)). (a)
coandirates
Repitive
ereninn
Velsary: furn).
(paative
negphive
Quan22 (fai)
Autobrive R' (success)
"OSsttacees et ue connet
Quend
There throuh stop-by stsepp
Vioual Anolysto pedestrian
Dinection steering ingle regettvee indieening
the road the left side. "hoffic Signa"" The froffic
wxisle
stotu NWW 2 Motton Modeing- intettoica Detot" The viicce has been accalanating and turning eif. The maarage eccaleration con be colculated from the
provied dene- Average
WM dog Wofreet 1.5
1.500) Varpnox
WW (mg yi= Vidrecf-l 80 16 122 Vimme Vessttins)
Weeerinn/s Wy
Viteer(n) WV 1X y(1-)) 0.0 a 32 Vi mes 05 05 Vitne (113) Vitines (0.522 2 Vaggras 2.5 V Vitection) Viatee 3 Log Deductions **Solety Checktn The vehicle turnnng left and eccelerating pedesttain cressing on the left, which could be patertia collsion risk The car atood is moving shroght, so thene ii iemedaate collsioe rish with it. sind
1.3 Vatimes 35
Viteeele/s Wi- repttiio Pradictiont W (ott)) 0.- 9,26 Wirme 0.5 0.5
if
"Asoumption Check" predicted position nod mare 4, Reflectior eccoleration histery
ochivoble nn/5, the accelonaioon paol fet Final Predictioiessed on the coloulatiens and the trojectery, the vchicle wll continue tum left and accelorute. The pozes ia cressing the left coll pere -o 2.51] (3.84. 2.76). (4.15, 3.01)//eccwe>>"
(x:3.55
the vihicle should be contoossmmmisl
Autobriee-R mon vinss orelya manage onalyns neeults: vehice interded diection the vhhill oppears to be prepering for o sight lnf- turn or ma straght path, the sheering any nagat saall reed markings and traffic lights nugest stroght gath sherd. ebstacie ex shead:
meddate abstacke cara
proceed. vehice contins fonward vap 22 mottor medeingh Vasing hererisal data ""past veh statuer 7
light
hi (2r-17 85, y--094) 94, vi 92mis on ft 0.26)//s tu (e--18 yod 66] vs Stre on(0 4 m//5mms hi (eed D. yed 0) vi 78m/s 0.0 3535/ss caluuattora average noce brutton q_ x (0 0.05 0.7
Gid 0.77// peottion
0/0977 000m/a 2
3817 -S4nn's -eeboty predictinn v.ai 578 006 5.6mm (054) velicle
(0.5)* follownn trojectory
offeet:
teen "33 deductions
red ligh??
the
treffic light le green collid with dheedP
webble sheod sedestriar cropsing no no pedeemiioe are wittle condusion: recommended oction refiicctooonn widee Vislidation asoumpttin chuck "peddcted pos (xr2 yc-6.16) yoe
cortine moving forword sight enj stroonts for
fum
requres everage the
vhhicle dhoud for the noxt 3 are onnsenn 12 91, -0 (1166 15.72 1515 (8.33 1815 (10 74, 1.19. (12 s6, a [18.36 16dj/arseer the verick should continue moxing forward with dight adgutmerts for the left turn esdaring sol elly and odherence to traffic /itarr/ -anaver- 0.06). 19.72. 0.15). (8.33. [10.74, 0.19). 0.76), [15.5 1.66)//rowwr>"
feasblee
reed for
obetodiee charges treffic conditionn. ( wew
"*fial pradection breed the ondyytc the prod futurn
Figure 7: Visualization comparison between Qwen2.5-VL-- and AutoDrive-Ra on nuScenes.
22


Reviews:
[{"review_id": "9v6ZfnoLJp", "reviewer": "Reviewer_3jDT", "summary": "The application of VLA models in the field of autonomous driving has been attracting increasing attention. Among their capabilities, reasoning and self-reflection play a pivotal role. This paper trained a VLA model using a combination of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to equip it with enhanced reasoning and reflective abilities. Specifically, This paper introduces a reasoning dataset that incorporates a Chain-of-Thought (CoT) process, along with a physics-compliant reward framework to guide model training.", "questions": "- The paper primarily trains a VLA model to predict future vehicle trajectories. What advantages does this approach offer over rule‑based prediction and purely data‑driven prediction methods? VLA models, trained with textual data, indeed have strengths in language understanding and reasoning, but they tend to be limited in precise numerical computation. Would employing a VLA model for fuzzy decision‑making be more appropriate?\n- The paper uses L2 error to indirectly assess the performance improvement of the trained model. This provides some evidence of data and reward effectiveness; however, more direct methods demonstrating gains in reasoning, computation, and reflection would make the contribution clearer.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The authors construct a dataset and a reward framework grounded in practical problems; overall, the work is solid and technically sound. The paper makes clear contributions in dataset design and in the formulation of the reward function.", "weaknesses": "While the authors identify salient issues with VLA models in autonomous driving and present technically solid efforts to address them, the overall solution exhibits limited novelty: it defines conventional tasks, applies standard approaches, and consequently yields expected results. The paper would benefit from a clearer articulation of the challenges encountered during the design of the solution and the insights gleaned from addressing them.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pKjxp7LxJA", "reviewer": "Reviewer_okCa", "summary": "This paper presents AutoDrive-R², a novel Vision–Language–Action (VLA) framework for autonomous driving that aims to enhance both reasoning interpretability and trajectory feasibility. Supervised Fine-Tuning (SFT) using a new Chain-of-Thought dataset (nuScenesR²-6K) that instills structured reasoning and self-reflection. Reinforcement Learning (RL) using Group Relative Policy Optimization (GRPO) with a physics-grounded reward framework that integrates spatial alignment, steering dynamics, and temporal smoothness, ensuring physically feasible trajectories. The method is evaluated on nuScenes and Waymo benchmarks, achieving state-of-the-art (SOTA) performance with substantial reductions in L2 trajectory errors compared to EMMA+ and DriveVLM, and demonstrating strong zero-shot generalization. The work positions itself at the intersection of multimodal reasoning and embodied intelligence, extending recent reasoning-incentivized LLM/RL techniques (e.g., DeepSeek-R1) to the autonomous driving domain.", "questions": "1. How does AutoDrive-R² perform under closed-loop simulation or in real-world deployment (e.g., CARLA)? Would the physics-grounded reward suffice without explicit simulator feedback?\n2. How were the CoT reasoning steps validated for correctness or consistency during dataset construction? Were human checks performed post-Qwen2.5-VL synthesis?\n3. Could you show qualitative examples where AutoDrive-R² fails (e.g., multi-agent interactions or occlusions) and explain whether the reflection step mitigates or worsens these?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Introduces reasoning-incentivized CoT learning and self-reflection for VLA models — a clear conceptual advance over purely data-driven planners.\n2. Sound integration of SFT and GRPO with well-formulated physics-aware rewards; rigorous ablations confirm design choices.\n3. Large quantitative gains on nuScenes (Avg L2 error 0.19 m vs 0.29 m for EMMA+) and Waymo (0.20 m vs 0.30 m), plus zero-shot transferability.\n4. The four-stage reasoning framework (Observation–Calculation–Logic–Reflection) is both intuitive and interpretable.", "weaknesses": "1. Although the nuScenesR²-6K dataset is novel, 6k samples may not capture full traffic complexity. The manual synthesis with Qwen2.5-VL could introduce distributional bias.\n2. Experiments are open-loop trajectory predictions; it remains unclear how the system performs in end-to-end control or real-time deployment.\n3. The GRPO ablations demonstrate quantitative gains, but lack qualitative discussion on how each reward term alters trajectory smoothness or safety metrics.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2Z8edGwrcE", "reviewer": "Reviewer_TqXQ", "summary": "This paper proposes AutoDrive-R^2, a novel VLA framework designed to enhance the reasoning and self-reflection capabilities of autonomous driving systems, addressing the limitations of existing methods such as physically infeasible trajectory generation and inadequate reasoning for complex scenarios. The framework adopts a two-stage training approach: in the first stage, a CoT dataset named nuScenesR^2-6K (with 6,000 image-trajectory pairs) is constructed for SFT, which guides the model through a four-step logical chain to build cognitive connections between input information and output trajectories. In the second stage, a physics-grounded reward framework integrated with GRPO is employed for RL, incorporating spatial alignment, vehicle dynamics, and temporal smoothness constraints to ensure trajectory feasibility. Experimental results on nuScenes and Waymo datasets demonstrate that AutoDrive-R^2 achieves state-of-the-art  performance and robust zero-shot generalization.", "questions": "For the dataset: How were the manual annotations of the CoT reasoning process validated? How to ensure the consistency of reasoning steps? Additionally, since the dataset is derived from nuScenes, does it inherit the scene bias of the original dataset (e.g., urban road dominance), and if so, how does this affect the model's generalization to non-urban scenarios (e.g., highways or rural roads)?\n\nThe paper sets all weight coefficients (λ_pos, λ_ste, λ_vel, λ_tem) to 1 in experiments. Have you tested the impact of different weight combinations on performance? Besides, have you analyzed the impact of GRPO hyperparameters (e.g., number of candidate responses, beta in KL-divergence) on trajectory prediction accuracy and training stability?\n\nFor the zero-shot performance on Waymo, this paper attributes the excellent zero-shot generalization to the model's structured reasoning capabilities, but it does not compare with other methods that also claim zero-shot adaptation. Could you explain why your method has a more significant zero-shot advantage, and whether the dataset contains scene features that are common to both nuScenes and Waymo?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "nuScenesR^2-6K is the first dataset in autonomous driving that integrates self-reflection for validation, providing detailed reasoning chains to bridge input information and output trajectories, which enhances model interpretability.\n\nThe combination of SFT with structured CoT reasoning and RL with physics-grounded rewards effectively addresses both reasoning inadequacy and physical infeasibility of trajectories\n\nAutoDrive-R² outperforms state-of-the-art methods on nuScenes and Waymo datasets, with significant error reductions and robust zero-shot capabilities, validating its effectiveness and generality.", "weaknesses": "The RL stage with GRPO requires generating multiple candidate responses, which may introduce high computational overhead, but the manuscript does not discuss inference speed or real-time deployment feasibility.\n\nAlthough the paper emphasizes the role of self-reflection in the four-step reasoning chain, it does not clearly explain how the model corrects inconsistent trajectories during self-reflection. For example, there is no detailed description of the decision rules (e.g., threshold for determining trajectory inconsistency) or the specific adjustment strategies (e.g., how to modify velocity or steering angle) adopted in the self-reflection stage.\n\nThis paper does not analyze the computational overhead (e.g., inference time per trajectory, GPU memory usage) or compare it with lightweight baseline methods. This may restrict the practical deployment on edge devices with limited computing resources.\n\nTable 2 appears to be outside the page content", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "R8xkuVOvX3", "reviewer": "Reviewer_vs3C", "summary": "This paper proposes a method based on VLAs for trajectory prediction in autonomous driving. The proposed method consists of a two-stage process: 1) a chain-of-thought dataset based on NuScenes used for fine-tuning of an existing VLA (the paper uses Qwen 2.5 VL) so that it learns to \"think\" through the trajectory generation; 2) a reinforcement learning stage based on GRPO to align the model's predictions with physical trajectory properties (e.g. position) and model output format. The method is evaluated on the NuScenes and Waymo datasets and compared to other methods and ablated baselines, showing significant gains in positional accurate of predicted trajectories.", "questions": "Q1: The \"self-correction\" examples presented in appendix A.3 are not clear to me. In what sense does the red text in Fig4 show an \"Aha moment\"? I can't tell what error was detected and how it was corrected.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The paper builds on recent progress in using VLAs for trajectory prediction, improving model accuracy.\n- The proposed dataset is novel and could be broadly useful beyond this particular work.\n- The empirical evaluation shows significant gains.\n- The writing is clear (for me).", "weaknesses": "Overall I enjoyed reading this paper, though I noticed a couple of issues:\n\n1) Limited metrics used in evaluation: the evaluation only uses a L2-based metric on predicted vs true positions. But it does not measure other important aspects, such as feasibility (based on vehicle model) or rule/constraint violations of generated trajectories. Prior literature on trajectory prediction has developed a number of metrics to provide a more complete picture (see next point).\n\n2) Limited discussion of work prior to VLA: related to above comment, I find that the paper lacks discussion of works prior to the recent VLA-based research. There is a large body of work on trajectory prediction in autonomous driving, and it would help readers to see the relationship with prior works. For example, prior works have developed evaluation metrics for trajectory prediction e.g. [1,2]; and there is also prior work on explainable AI in autonomous driving e.g. [3,4], which is one of the stated motivations for the proposed CoT fine-tuning method in this paper.\n\n[1] https://arxiv.org/abs/2210.06106 (RAL 2023)\n\n[2] https://arxiv.org/abs/2203.08251 (IROS 2022)\n\n[3] https://arxiv.org/abs/2402.10086 (T-ITS 2024)\n\n[4] https://arxiv.org/abs/2002.02277 (ICRA 2021)\n\nI think the paper could be strengthened by addressing these two aspects.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)