Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving
Authors: Pengxiang Li
Yinan Zheng
Yue Wang
Huimin Wang
Hang Zhao
Jingjing Liu
Xianyuan Zhan
Kun Zhan
XianPeng Lang
Venue: ICLR 2026 Poster (2026)
Keywords: autonomous driving
VLA
discrete diffusion
Primary area: applications to robotics, autonomy, planning

Abstract: End-to-End (E2E) solutions have emerged as a mainstream approach for autonomous driving systems, with Vision-Language-Action (VLA) models representing a new paradigm that leverages pre-trained multimodal knowledge from Vision-Language Models (VLMs) to interpret and interact with complex real-world environments. However, these methods remain constrained by the limitations of imitation learning, which struggles to inherently encode physical rules during training. Existing approaches often rely on complex rule-based post-refinement, employ reinforcement learning that remains largely limited to simulation, or utilize diffusion guidance that requires computationally expensive gradient calculations. To address these challenges, we introduce ReflectDrive, a novel learning-based framework that integrates a reflection mechanism for safe trajectory generation via discrete diffusion. We first discretize the two-dimensional driving space to construct an action codebook, enabling the use of pre-trained Diffusion Language Models for planning tasks through fine-tuning. Central to our approach is a safety-aware reflection mechanism that performs iterative self-correction without gradient computation. Our method begins with goal-conditioned trajectory generation to model multi-modal driving behaviors. Based on this, we apply local search methods to identify unsafe tokens and determine feasible solutions, which then serve as safe anchors for inpainting-based regeneration. Evaluated on the NAVSIM benchmark, ReflectDrive demonstrates significant advantages in safety-critical trajectory generation, offering a scalable and reliable solution for autonomous driving systems.

Published as a conference paper at ICLR 2026
DISCRETE DIFFUSION FOR REFLECTIVE VISION- LANGUAGE-ACTION MODELS IN AUTONOMOUS DRIVING
Pengxiang Li Yinan Zheng?", Yue Wang"", Huimin Wang"
Hang Zhao',
, Jingjing Liu?, Xianyuan Zhan', Kun Zhan', Xianpeng Lang'
LiAuto
Tsinghua University
ABSTRACT
End-to-End (E2E) solutions have emerged as mainstream approach for au- tonomous driving systems, with Vision-Language-Acttin (VLAA) models repre- senting new paradigm that leverages pre-trained multimodal knowledge from Vision-Language Models (VLMs) interpret and interact with complex real- world environments. However, these methods remain constrained by the limita- tions of imitation learning, which struggles to inherently encode physical rules during training. Existing approaches often rely complex rule-based post- refinement, employ reinforcement learning that remains largely limited to sim- ulation, or utilize diffusion guidance that requires computationally expensive gra- dient calculations. To address these challenges, we introduce ReflectDrive, a novel learning-hand based framework that integrates reflection mechanism for safe trajec- tory generation via discrete diffusion. We first discretize the two-dimensional driving space to construct an action codebook, enabling the use of pre-trained Diffusion Language Models for planning tasks through fine-tuning. Central our approach safety-aware reflection mechanism that performs iterative self-correction without gradient computation. Our method begins with goal- conditioned trajectory generation to model multi-modal driving behaviors. Based this, apply local search methods to identify unsafe tokens and determine feasible solutions, which then serve as safe anchors for inpainting- regen- eration. Evaluated nificant advantages in safety-criticll trajectory generation, offering scalable and reliable solution for autonomous driving systems.
the NAVSIM benchmark, ReflectDrive demonstrates sig-
INTRODUCTION
Autonomous driving (AD) is guiding the transportation industry toward safer and more efficient future (Tampuu et al., 2020). Within this trend, End-to-End (F2E) systems (Hu et al., 2023; Chen al., 2023) have emerged as the mainstream alternative traditional modular designs (Bansal et al., 2018), which are prone to error accumulation between interdependent modules. They have also largely replaced rule-based methods (Fan al., 2018; Treiber et al., 2000) that demand ex- tensive human engineering effort. Meanwhile,
(VLA) models (Kim et al., 2024: Hwang al., 2024) offer a new solution by incorporating pee-trriieeee trained knowledge from Vision- Language Models (VLMs) (Hurst et al., 2024; Bai et al., 2025). Equipped with enhanced general- ization capabilities, VLA models can interpret visual scenes and understand human instructions to directly output planning trajectories, thereby improving adaptability in challenging situations.
Aision-Laguugge-Aee
However, eixsting learning-based methods does not resolve the core challenge in imitation learning- based driving systems. Specifically, behavior cloning fails to inherently encode inviolable physical rules, such as collision avoidance or adherence to drivable areas (Lu et al., 2023). As a result, a generated trajectory may be highly probable under the model's distribution yet still violate critical safety constraints. Consequently, existing deployed solutions often rely on significant human pri- ors, such as trajectory anchors (Li al., 2024) or rule-based generated paths (Dauner et al., 2023).

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These priors offer reliable initial solution for the learning system, but they also necessitate sub- stantial proseerocsssnng particularly in complex scenarios. Concurrently, more advanced solutions emerging. Some methods integrate reinforcement learning (Kaelbling et al., 1996; Kendall et al., 2019; Jaeger et al., 2025; Cusumano-TTower et al., 2025) with human-designed reward functions to enhance causal reasoning. However, most existing studies remain confined to the simulation level. From a deployment perspective, these approaches typically require unsafe online rollouts and suffer from training instability, especially in large-scale models (Zheng et al., 2024). Although guidance mechanisms in diffusion models provide promising alternative by enabling controllable generation during inference (Zheng et al., 2025; Jiang et al., 2023; Zhong et al., 2023), they often experience slow sampling speeds due to gradient computations and are highly sensitive to parameter tuning, which can lead to numerical instability.
To address these challenges, we pioneer the use of discrete diffusion (Austin et al., 2021) for plan- ning to meet the demand for verifiable and controllable E2E driving systems. A key advantage of this approach is its operation in discrete action space, which facilitates the seamless incorporation of critical safety constraints through search, masking, and sampling techniques during trajectory generation. This results in hybrid framework in which learned behaviors can be rigorously guided by prior knowledge, shifting away from black-box planning toward trustworthy and interpretable decision-making. Inspired by these insights, we propose ReflectDrive, a novel tearning-based frame- work that integrates a reflection mechanism for safe trajectory generation via discrete diffusion. Specifically, we first discretize the two-dimensional driving space construct action codebook, enabling the representation of vehicle trajectories through discrete codebook embeddings. This rep- resentation allows us to leverage a pre-trained Diffusion Language Models (DLMs) (You et al., 2025; Nie et al., 2025) for planning tasks via fine-tuning. The approach facilitates parallel decoding and bidirectional feature fusion within a unified architecture that supports scalable training. Based on this fine-tuned model, our reflection mechanism begins with goal-conditioned generation, where the goal point guides the generation process to capture diverse multi-modal driving behaviors. Fur- thermore, the framework integrates safety metrics to evaluate the generated multi- trajectories. For unsafe waypoints, we perform a local search to identify a feasible solution, which then serves as safe anchor token for trajectory inpainting. The entire process operates without gradient computa- tion, enabling parallel generation and the injection of safety constraints during trajectory regenera- tion. Evaluations on the real-world autonomous driving benchmark NAVSIM (Dauner et al., 2024) demonstrate the feasibility of employing discrete diffusion for trajectory generation. Equipped with our reflection mechanism, RefleciDrive achieves near human-level closed-loop performance. Our contributions are summarized as follows:
We pioneer the application of discrete diffusion for E2E autonomous driving trajectory generation and integrate it into a VLA model for scalable training.
We introduce reflection mechanism, novel pnterence-timee guidance framework specifi- cally designed for the denoising process in discrete diffusion, integrating external safety validation with efficient discrete token optimization.
We evaluate our method on real-world driving benchmarks, proving that the framework can enforce hard safety constraints without compromising behavioral coherence.
2 RELATED WORK
End-to-End Autonomous Driving. E2E methods (Hu et al., 2023; Chen et al., 2023) have emerged promising solution to largely replace rule-based approaches due to their superior scal- ability. Recently, VLA models (Hwang et al., 2024; Renz et al., 2025; Zhou et al., 2025) have arisen as new paradigm, incorporating world knowledge from pre-trained VLMs to enhance performance in long-tail scenarios. Additionally, VLA architectures can accept human instructions to support human--refernt driving behaviors (Kim al.. 2024), while language serves as an interpretable intermediate representation for improved explainability (Tian et al., 2024; Wang et al., 2025).
Beyond Imitation Learning. Curent mainstream pipelines still operate within imitation learning- based frameworks, which suffer from causal confusion and lack verifiable safety guarantees. Many studies have attempted to address this issue, which can be broadly categorized as follows: 1) The model uses trajectory anchors, which are derived from clustered trajectory data rule-based
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proposals, conditioning inputs and is designed to predict offsets for further trajectory refine- ment (Dauner et al., 2023). Hydra-MDP (Li et al., 2024) utilizes trajectory anchors as candidates for post-selection, while DiffusionDrive (Liao et al., 2024) employs anchors as starting points and uses a pseudo-diffusion process for refinement. Although these methods exhibit improved relia- bility, they rely heavily rule-based design. 2) Reinforcement learning methods enhance model capabilities through exploration (Shalev-Shwartz et al., 2016; Kiran et al., ;021; Cao et al., 2023; Lu al., 2023); for instance, GIGAFLOW Iissumano-Towmrg et al., 2025) significantly improves per- formance via self-play in simulation. However, online rollouts are infeasible for real- weal-world vehicle deployment, and simulation training faces the sim-to-real gap. Although recent advances in world models (Guan et al., 2024) offer potential solution, they still struggle with out-of-distribution sim- ulation. 3) Other methods, such as guidance mechanisms for diffusion models, enable the injection of reward signals during the denoising process (Jiang et al., 2023; Zhong et al., 2023). Diffusion Planner (Zheng et al., 2025) represents a pioneering effort in applying diffusion models to closed- loop planning tasks. Although it utilizes guidance to adjust behavior during inference, the method relies on additional gradient computations, resulting in high computational cost. In this paper, we propose novel reflection mechanism based on discrete diffusion that naturally incorporates safety constraints through search, masking, and inpainting during trajectory generation.
3 PRELIMINARIES
3.1 AUTONOMOUS DRIVING PLANNING
We formulate the autonomous driving planning task as learning a conditional distribution p(r c). where the goal is to generate a future trajectory т. Each waypoint is expressed in the ego-vehicle frame, conditioned on a scene context that includes multi-view images, instructions, and ego- vehicle state. The primary challenge in planning is that trajectories must adhere to traffic rules and safety constraints, which is difficult for imitation learning- methods due to the absence of explicit signals to ensure strict compliance with these requirements.
3.2 DISCRETE DIFFUSION
Discrete diffusion models (Austin al., 2021; Meng et al., 2022; Lou et al., 2023) have emerged as powerful non-autoregresssvve paradigm for generating structured sequences. This process is defined by forward corruption process and learned reverse denoising process.
Forward and Reverse Process. The forward process degrades a clean sequence of discrete tokens
y (y....... ................................ ,....., y)) over series of S timesteps. At each step (l...., S). a noisy version of the sequence, y((), is created by masking a subset of the tokens in y. Specifically, a binary mask (0) = (m "..",..,,."" (e)
"........] E 1) is sampled, and each token y; is replaced with The number of masked tokens determined by noise
special [MASK] token if m( schedule, such as cosine schedule, which typically increases the masking ratio as approaches S. The core learning task is to train a model Po to reverse this corruption. This model learns to predict the original tokens at the masked positions, conditioned on the unmasked tokens, the timestep s, and any external context e. The model is trained by minimizing the negative log-likelihood objective:
[()) = R.......
log poly: (00) ,,,))),)))))))))))
Emm
Here, s E [0, 1] represents the masking ratio determined by the noise schedule, and e encompasses the scene context including multi-view images, ego-status, and instructions.
Model Inference. To generate a new sequence, the process starts with a fully masked sequence, (8)). The model then iteratively refines this sequence for S steps. In each step, the model predicts probability distribution for the tokens the masked positions. A subset of these predictions is then sampled and fixed, while the rest are re- re-masked for the next refinement step. Specifically, we utilize linear noise schedule. During inference, we adopt parallel decoding strategy where,
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(a) Model Architecture
<x1, yt>
<x5, y5>
<x8, yo>
Diffusion Language Models
Vision Encoder
Text Tokenizer
Masked Trajectory Tokens
Pleuse predict future trajectory
Trajectory Tokens
(b) Goal-Conditioned Generation
(c) Safety-Guided Regeneration
Trajectory Inpainting
G1
0.0
X
G2
0.4
High Boore
0.6
Figure I: ReflectDrive Framework Overview.
each step, we select and fix subset of tokens with the highest predicted confidence scores, allowing multiple tokens to be decoded simultaneously until the sequence is complete. A central advantage of this framework, and one especially critical to our work, is its capacity for inpainting, defined as the ability to reconstruct masked segments of sequence while maintaining consistency with the context from unmasked tokens. Additionally, the discrete token structure supports efficient search and constraint integration, making it possible guide trajectories using safety constraints.
4 METHOD
In this section, we present RefleciDrive, a novel learning-based framework that integrates a reflection mechanism facilitate safe trajectory generation via discrete diffusion, illustrated in Figure 1. We first introduce a trajectory discretization method tailored for integration into masked diffusion process. A pre-trained diffusion language model is then employed for trajectory generation. Finally, we propose a reflection mechanism specifically designed to ensure safety during the trajectory gen- eration process. This mechanism leverages diffusion inpainting and capitalizes on the advantages of discrete token spaces for efficient constraint -assd search.
4.1 DISCRETE DIFFUSION FOR AUTONOMOUS DRIVING PLANNING
Trajectory Discretization. To represent continuous waypoints in discrete format, we quantize each 2D coordinate (z. by mapping its x and y values independently to the closest tokens their respective ID codebooks. We define a uniform ID codebook A {a) ....... } by discretizing spatial range [-M, M] with resolution Ag. quantizer Q maps a real value its nearest token, and its inverse recovers the coordinate. Each 2D waypoint is thus represented by token pair (y jas Y Vi.)). and the full trajectory becomes a flattened sequence y = Q(r) = (y1.s. fy.............................. .N. y e A At first glance, discretization may appear to cause some loss trajectory precision. However, in practical deployment, the resolution can be adjusted to control accuracy, different codebook partitioning strategies can be employed. Specifically, we utilize a grid resolution of Ag = 0.3 me- ters. Given the spatial range of [-100, 100] meters, this results in codebook size of |A| z 667 tokens per dimension. Most importantly, discretization facilitates efficient search for feasible solu- tions in the Bird' s-Hye View (BEV) space. Experimental results in Section 5.2 and Figure 3 further

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demonstrate that, with discrete representations, our reflection mechanism significantly enhances the safety of the generated trajectories.
Discrete Diffusion Model. Based our discretized trajectory representation, we instantiate the trajectory planner using the discrete diffusion framework described in Section 3. In practice, we employ VLA model as the planner, initialized from a pre-trained Diffusion Language Model (You et al., 2025; Nie et al., 2025) that exhibits strong pre-training performance in understanding driving scenarios. The model can generate tokenized trajectory y conditioned scene context e (multi- view images, language instruction, ego state). The model is trained via the denoising objective in Eq. 1 using autonomous driving planning datasets for supervised fine-tuning. This provides the inherent capability for bidirectional inpainting, which serves as the foundation of our method. It enables the model to perform holistic parallel refinement and elegantly repair trajectories around externally guided safety edits during the reflective inference process.
4.2 REFLECTIVE INFERENCE
With the discrete diffusion- based VLA model as our foundation, we introduce a reflective inference framework bridge the gap between imitation learning and safety-critical deployment. This frame- work operates in two stages: goal-conditioned trajectory generation and safety-guided regeneration. The entire process is guided by a of specialized scoring functions.
Scoring Function Definitions. To systematically evaluate trajectories, our framework incorpo- rates three distinct scoring functions. The detailed composition of these functions, which are de- signed based on established autonomous driving evaluation principles, is provided in Appendix E.
Global Scorer (S (T)) This scorer evaluates the overall quality of a complete trajectory, con- sidering both safety and coherence, and returns value of zero any critical rule is violated.
Safety Scorer (Suge (T)): This scorer acts safety oracle to identify specific points of failure.
Local Scorer (Stveal ay))) This scorer evaluates each candidate token pair (az, dy) using a comprchensive function that assesses its impact on the trajectory's 's safety and coherence.
Coal-Conditioned Generation. To ensure our planner can reason about high-level, global intents that go beyond simple local adjustments, the process begins with generating diverse set of trajec- tory proposals. This procedure is essential for multi-modal driving behavior modeling and serves a necessary step for subsequent regeneration. Since the local search in our safety-aware regen- eration stage is intentionally constrained for efficiency, it cannot accommodate large-scale changes, such taking different turn at an intersection, which require broader exploration. We first use the model to produce a probability distribution for the terminal waypoint tokens, pe (y n c, 6), where yN N
From this distribution, we sample set of high-probability goal candidates. We then apply Non- Maximumm Suppression (NMS) (Ren et al., 2015) to obtain spatially diverse set of K candidate goals. (GI...., G
G NMS (TopK K: (po(yN) (c,,s), dNMS: K)
where TopKx<()) is an operator that selects the K" most probable goal candidates from the model's output distribution. The NMS(.) function then filters this set using a distance threshold dyms to produce the final, spatially diverse set G of size K. For practical deployment, dedicated goal generation model could be used to improve the accuracy and quality of goal points. However, for simplicity, employ the same model for both goal generation and trajectory planning. Then, for each goal G₁ E G. we generate a full trajectory тı by sampling from the conditional distri- bution Po 0y G,, via inpainting. The resulting K trajectories Global Scorer Selobal"). which assesses each plan based on a combination of metrics including goal progress. The top-scoring trajectory 7" is then selected for further refinement.
evaluated using the
T arg max S(Tx).
(3)
Tk k= L..,K..KKK
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Trajectory Evaluation
Safety Anchors Search
Trajectory Inpainting
0.D
in 00 Safety Anchors
Scorer
Diffusion Language Models
Figure 2: Safety-Guided Regeneration Pipeline.
Safety- Guided Regeneration. The selected trajectory r", while coherent, may still violate physi- cal constraints. We address this with an iterative, gradient-free refinement loop that forms a dialogue between the generative model and external safety oracle, shown in Figure 2.
Trajectory Evaluation. The process begins when the Safety Scorer S evaluates the de- quantized trajectory and identifies the specific waypoints that are unsafe. The oracle assigns safety score to each original waypoint based on the worst violation (e.g., drivable area infraction) within a local time window. This allows precisely pinpoint unsafe waypoints.
Safety Anchors Search. For the earliest waypoint that violates safety threshold, we perform highly efficient local search within small Manhattan neighborhood Ng of the original tokens to identify an improved token pair, rather than resorting to complex continuous optimization. The corrected token pair that maximizes the local safety score is then designated as a safety anchor.
Trajectory Inpainting. We then leverage the diffusion model's powerful inpainting capability to regenerate the surrounding trajectory segments conditioned on safety anchors. This single-pass regeneration allows the model to naturally re-establish global coherence around the safety driven edit. This cycle of identifying violations, performing discrete corrections, and re-inpainting con- tinues until the plan is fully safe computational budget met. Specifically, set maximum budget of 10 iterations to ensure real-time feasibility. If the algorithm reaches this limit without finding a fully safe trajectory, outputs the candidate with the highest safety score found during the search as a fallback strategy.
This refinement process operates as an iterative loop. In each iteration, the top-scoring trajectory 7* is evaluated by the Safety Scorer to identify the set of all violating waypoint indices V is empty, the trajectory is deemed safe and the process terminates. Otherwise, we select subset 7 C corresponding to the segment of violations. For each timestep 1 E T*, the Local Scorer is employed to find an improved token pair within a local neighborhood
{E (T)e(")): < Tsafe);
N/ by solving:
(Yi., Vt.g) = arg max Stocal ay). (o. D.G.,,,)Y.Gy........
(4)
The original tokens at the indices T are replaced by these optimized pairs, which serve as fixed safety anchors for the subsequent inpainting step. The refinement cycle then continues with this updated trajectory. In practice, the reflective inference process is designed for real-time performance. The local search for corrective tokens is efficient, as it operates over small, discrete neighborhood (e.g., Manhattan distance d < 10) rather than requiring expensive gradient- -based optimization. In practice, we find that most safety violations are resolved within 1-3 iterations reflection, resulting in manageable inference overhead.
5
EXPERIMENTS
BENCHMARK AND BASELINES
Evaluation Setups. In our implementation, the VLA model backbone is initialized from publicly available pre-trained Vision-Language Model (LLaDA-V You et al. (2025)) and utilizes classifier- free guidance for trajectory generation. Input images obtained from the front, front-left, and

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Table NAVSIM Closed-Loop Results. Methods are grouped by their core architectural paradigm. The symbol denotes our method using privileged ground-truth oracle for reflection, serving as an analytical upper bound. Best result per column in bold (higher is better).
Method
Paradigm
Input
NCT
DACT
TTCT Comf.t
EPT
PDMST
Base End-to-Bi Planners
UniAD
Cam
97.8
91.9
92.9
100.0
78.8
83.4
PARA-Drive
Cam
97.9
92.4
93.0
99.8
79.3
84.0
Transfuser
C& L
97.7
92.8
92.8
100.0
79.2
84.0
Augmented End-to-End Planners
Hydra-MDP
C& L
98.3
96.0
94.6
100.0
78.7
86.5
DiffusionDrive
Diffusion
C& L
98.2
96.2
94.7
100.0
82.2
88.1
GoalFlow
Diffusion
C&
98.4
98.3
94.6
100.0
85.0
90.3
VLA Planners
VLA (Post-RFT)
Autoregressive
Cam
98.4
95.6
99.9
81.9
89.1
ReflectDrive (w/o R.I.)
Diserete Diffusion
Cam
96.9
95.4
92.2
100.0
79.0
84.8
ReflectDrive (Ours)
Discrete Diffusion
Cam
97.7
99.3
93.5
100.0
86.9
91.1
ReflectDrive
Discrete Diffusion
Cam
99.7
99.5
99.1
99.9
88.9
94.7
Human
100.0
100.0
100.0
99.9
87.5
94.8
front-right cameras. The language instruction provides a high-level navigational command, such as "turn left" or "go straight," along with textual descriptions the ego vehicle's status. We eval- uate model the large-scale real-world autonomous driving benchmark NAVSIM (Dauner et al., 2024) for closed-loop performance assessment. Following the official protocol, performance reported with the PDMS score (higher is better), aggregated from five metries: NC (no-collision rate), DAC (drivable area compliance). 77C (time-to-collision safety), Comfort (bounded accelera- tion/jerk) and EP (ego progress). We run all the methods under the official closed-loop simulator and report averages on the public test split. Our planner uses camera-only inputs unless otherwise stated; we also include Camera+LiDAR baselines provide a more comprehensive comparison.
Baselines. We compare ReflectDrive to other autonomous driving systems. For example, vanilla E2E planners that purely use sensor information as input and output trajectories, such as UniAD (Hu et al., 2023), Para-Drive (Weng et al., 2024), Transfuser (Chitta et al., 2023). As well as augmented E2E planners that incorporate clustering results as auxiliary information like Hydra-MDP (Li al., 2024), DiffusionDrive (Liao et al., 2024), and GoalFlow (Xing et al., 2025), the PDMS scores will be higher than vanilla E2E planners due to additional information. We also include recent AutoVLA (Zhou et al., 2025) model that unifies reasoning and action generation within sin- gle autoregressive generation model, the PMDS score is the highest among VLA planners. For our model family, the table lists: ReflectDrive (w/o R.I.) trained with discrete masked diffusion adding classifier-free guidance at inference without reflective inference; ReflectDrive adding goal- conditioned generation and safety-guided regeneration, where the safety-guided regeneration relies on the reward model where surrounding obstacles are moving at constant speeds; ReflectDrivel adding goal-conditioned generation and safety-guided regeneration, where the safety-guided regen- eration relies on the reward model where surrounding obstacles are ground- agents.
5.2 MAIN RESULTS
Evaluation results on the NAVSIM benchmark are presented in Table
Base Model Validation. ReflectDrive base model achieves the PDMS score 84.8 comparable to the base end-to-end models, such as UniAD, PARA-Drive, and Hydra-MDP, and slightly lower than the score of Augmented End-to-End End Planners. However, it has not yet demonstrated significant per- formance advantages. We identify two potential limiting factors: first, the limited scale of training data, and second, room for improvement in the base VLM model's capabilities.
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Violation of drivable aera compliance corrected by S.G.R.
COO www. ondeg sprdict
Violation of time-to-collision corrected by S.G.R.
Figure 3: Safety-Guided Regeneration (S.G.R) Visualization. The first row illustrates three sce- narios where large-angle turns are prone to boundary violations. The initial trajectories (lightest color) carry the risk of exceeding the boundaries. Using S.G.R, the trajectory is gradually optimized toward the safe region (with its color darkening progressively), ultimately resulting in feasible trajectory. The second row depicts three scenarios involving intense interactions. Initial trajectories may pose collision risks with other vehicles or pedestrians. Through the iterative optimization of S.G.R., the trajectories learn to avoid conflicts or decelerate to yield, achieving much higher safety.
Significant Improvements from Reflective Inference. The introduction of safety-guider regen- eration mechanism yields substantial improvements in safety metrics such as DAC, TTC and NC. This is primarily due to our reward function design that fully considers safety-related factors. For EP metrics, we employ goal-conditioned generation strategy for optimization. Compared to Reflect- Drive (w/o R.I.). DAC gets +3.9-point improvement,77C gets +1.3-point improvement, NC gets +0.8-poim improvement and EP gets +7.9-point improvement. while ensuring trajectory safety without compromising progress. Compared to other end-to-end planners, DAC significantly outper- forms others and approaches human-level performance, while 77C and NC underperform expecta- tions due to the use of vonstant-velocity agents, which can lead to inaccurate safety estimations in safety-critical scenarios. To explore the upper bound of RefleciDrive, we therefore employ ground- truth agent states in our evaluation.
Approaching Human Driving Performance. When using ground truth agents information (i.e.. with complete environmental information), the performance of the system already matches human driving trajectories, such as NC 99.7, DAC 99.5, TTC 99.1, even EP 88.9 which is higher than hu- man to demonstrate the potential powerful capabilities of ReflectDrive. Compared to ReflectDrive based on constant velocity agents, DAC gets +0.2-point improvement, TTC gets +5.6-point im- provement, NC gets +2.0-point improvement and EP gets +2.0-point improvement, which meet the expectations. This implies that further performance improvements can be achieved with more accurate detection and prediction results a concern that is mitigated in practical deployment, as specialized models are dedicated to these tasks. And through failure case analysis in Figure 6, identified optimization opportumities in the search algorithm. With further optimization the search algorithm, expect comprehensively surpass human driving performance.
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NAVSIM -nop
NAVSIM close dooed-loop results
NAVSIM closed-Inop
84.5
Score
84.0
87
Num. goal points
NMS threshold &
Sante
90.5
83.5
86
ReflectDrive (wio R.I.)
Exploration steps
85
Max iterations
63.0
7
11 13
17
19
0
10
steps Rellee R.I.)
o.G.C.G.
Param S.G.R.R.
Figure 4: Ablation on (a) the number of generation steps for RefleciDrive (w/o R.I.), (b) the number of goal points and range of NMS for Goal-Conditioned Generation (G.C.G.), and (c) the numbers of exploration steps as well as max iterations for Safety-Guided Regeneration (S.G.R.).
Table 2:
Ablation for Reflective Inference. The ablation study results of coal-conditioned genera- tion and safety-guided regeneration to demonstrate the effectiveness of reflective inference.
Method
Goal-Cond. Safety-Guided
NCT
DACT
TTC1
Comf.1
E.P.T
PDMS1
W/o Both
96.9
95.4
92.2
100.0
79.0
84.8
W/ Goal-Cond.
X
96.6
96.5
91.5
100.0
83.8
87.4
W/ Safety- Guided
98.1
98.9
94.8
99.9
84.1
90.3
Full Model
97.7
99.3
93.5
99.9
86.9
91.1
5.3 QUALITATIVE RESULTS
To further demonstrate the capabilities of ReflectDrive, we show the trajectory generation results of representative scenarios, as shown in Figure 3. ReflectDrive shows high-security trajectory gener- ation, where the initial trajectory has the risk of going out of bounds, but with reflective inference as guidance, the trajectory gradually iterates and optimizes toward the safe region, ultimately pro- ducing a feasible trajectory. It is noteworthy that the generated trajectories remain kinematically feasible and smooth even after discretization, further demonstrating the viability of using discrete diffusion for autonomous driving planning. We also provide additional good examples in Figure 5.
5.4 ABLATION STUDIES
Ablation Inference Parameters. We conducted ablation experiments key adjustable pa- rameters involved in the generation and reflection process, with results presented in Figure 4. These parameters include: Generation steps, which governs the number of steps for impainting trajec- tories in our discrete diffusion model; Num. goal points, indicating the number of selected goal points (i.e., the number of multi-modal candidates); Exploration steps, controlling the search range for candidate points (with larger values providing more correction space); and Max iterations, de- noting the maximum number of regeneration iterations. For diffusion generation steps, the results reveal a non mon-oonti relationship between performance and the number of steps: model perfor- mance improves during the initial steps, peaks at 5 steps, and subsequently declines with additional steps. Furthermore, we demonstrate that multi-modal behavior modeling can further improve model performance and offer wider range of options for selection. Lastly, we observe the presence of inference scaling: computational resources allocated to exploration and regeneration steps in- crease, model inference performance improves accordingly. The upper bound of this scaling may also depend on the strategy employed, indicating potential for further optimization in future work.
Design Choices for Reflective Inference. Based on the optimal parameter configuration, we conducted ablation experiments on goal-conditooned generation and safety-guided regeneration methods. As shown in Table 2, the results indicate that goal-conditioned generation enhances ego progress, while safety- -giiddd regeneration improves both safety metrics and progress perfor- mance. These findings validate the complementary nature of our ReflectDrive approach, where condicondit generation focuses on progress optimization while safety- guided regeneration en- sures safety constraints are met without compromising driving efficiency.

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6 CONCLUSION
We propose ReflectDrive, a novel learning-based framework that integrates a reflection mechanism for safe trajectory generation via discrete diffusion. The two-dimensional driving space is dis- cretized into action codebook, enabling fine-tuning of pre-trained Diffusion Language Models for planning tasks. Our reflection mechanism begins with goal-conditioned generation to capture diverse multi mulli-modal behaviors, followed by safety-guided regeneration that identifies feasible lutions through gradient-free inpainting. Evaluations on the NAVSIM benchmark demonstrate the effectiveness and safety advantages of our approach. Due to space limitations, further discussions on limitations and future directions are provided in Appendix G.
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APPENDIX
A VISUALIZATION OF PLANNING RESULTS
Figure 5: Planning results that meet the PDM evaluation criteria.
B SUPERVISED FINE-TUNING (SFT) DETAILS
Table shows the parameters used in our inference stage. We fixed the length the output be- cause the number of trajectory points is always the same, and we perform parallel decoding for all trajectory points. We generate 3 diverse goal proposals to ensure good coverage of potential driv- ing intents. threshold of 0.9 meters is used during non-maximum suppression ensure that the
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Figure 6: Planning results of bad cases. Row shows the oscillation between boundaries and needs to improve the reward, such as adding the distance from the centerline in the future. Row 2 shows goal point selection deviation. Row shows navigation deviation.
selected goal points are spatially distinct. The safety loop is capped at 10 iterations guarantee a fixed upper bound on inference time. In practice, most trajectories converge to safe state in model
1-3 iterations. Tab 4 shows the key hyperparameters in training stage. Specifically, was initialized from the pre-trained LLaDA-V checkpoint and fine-tuned on the navtrain split of NAVSIM, which contains 80k annotated samples.
Table 3: Inference Configuration for ReflectDrive.
Parameter
Value
Steps
Answer length
32
Block length
32
Remask
low-confidence
Number of goal candidates (K)
NMS distance threshold (dvus)
0.9
Max refinement iterations
10
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Table Key Hyperparameters for Training
Parameter
Value
Spatial Range (M)
[-100, 100]
Batch Size
Gradient Accumulation Steps
Learning Rate
x 10-6
Training Epochs
3
Max Context Length
8192
Learning Rate Scheduler
Cosine
Warmup Ratio
0.03
Weight Decay
0.0
Precision
bfloat16
C INFERENCE LATENCY AND SYSTEM PROFILE
We evaluate the latency and memory profile using a single NVIDIA H20 GPU. Our current im- plementation research prototype based the LLaDA-V backbone without engineering opti- mizations such as KV-caching, quantization, or C++ deployment. Table 5 details the inference time breakdown.
Table 5: Inference Latency Breakdown (Single NVIDIA H20).
Component
Time (s)
Note
Stage 1:
Goal-Conditioned Gen.
6.82
Total
Goal Proposal
0.62
Generation
Trajectory Inpainting
6.06
SScoring & Selection
0.15
Stage 2: Safety-Guided Regen.
4.88
Per Iteration
Safety Check
0.13
Search
0.84
Scoring & Selection
1.68
Regeneration (Inpainting)
2.14
Average Total Inference
8.92
The latency is currently dominated by the VLM backbone's forward pass and the Python-based search logic. The search overhead (2.52s) can be reduced to milliseconds through C++ optimization, and model inference can be accelerated using standard techniques such as KV caching. the worst- case scenario (max 10 iterations), the latency would be higher, but empirical results show that safety violations are resolved within 0.43 iterations on average.
D ADDITIONAL ABLATION STUDIES
D.1 DISCRETIZATION GRANULARITY
We investigate the impact of grid resolution Ag performance. As shown in Table 6, using an excessively fine granularity (Ag = 0. Im) leads to a significant performance drop (88.2). This is likely due to the drastically increased codebook size (|A) 2000), which complicates the classifica- tion task for the diffusion model. Conversely, the model exhibits robust high performance (> 90.7) across the range of 0.2m to 0.5m. We selected Ag 0.3m the default because it achieves perfor- mance comparable to the peak while maintaining significantly smaller vocabulary size, offering a better balance between precision and model complexity.
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Table 6: Ablation on Discretization Granularity (Ag).
Resolution Ag (m)
Codehook Size |
PDMS
0.1
2000
88.2
0.2
1000
91.3
0.3 (Default)
667
91.1
0.4
500
90.7
0.5
400
91.2
D.2 ROBUSTNESS OF SCORING FUNCTIONS
To address concerns about reward shaping, we conducted a sensitivity analysis on the scorer weights (WFP, WTTC, wc) and the binary TTC threshold. As shown in Table our method demonstrates strong robustness to hyperparameter variations. Removing specific weights (e.g., WEP 0) results in minimal performance fluctuation, confirming that the gains stem from the reflection mechanism rather than overfitting to metric weights.
Table 7: Sensitivity Analysis of Scorer Weights and TTC Threshold.
WEP
WTrc
w
TTC Thresh (s)
PDMS
5 (Default)
2
1.0
91.1
5
0
1.0
91.1
2
5
1.0
91.1
0
5
1.0
91.2
5
5
1.0
91.1
5
0
5
1.0
90.9
5
2
0.5
90.0
5
2
1.5
91.0
D.3 SENSITIVITY OF GOAL PROPOSAL PARAMETERS
In practice, we selected smaller number of goals (K = 3) from an efficiency perspective, requiring them to maintain a certain distance, and chose dNMs through visualization, rather than based on actual scores. Table confirms that K 3 captures the majority of the performance gains (+3.3 PDMS over = 1) while maintaining computational efficiency compared higher values like ==
We further illustrate this choice through qualitative analysis. Figure 7 demonstrates that relying solely on the Top-1 candidate (K 1, shown in Black) often leads failure cases, whereas increasing K to introduces valid alternatives (Top-2 and Top-3, shown Red). Additionally, Figure visualizes the effect of the NMS threshold. While larger threshold (2.1m) increases spa- tial diversity compared to our default (0.9m), introduces candidates with higher variance that can be challenging for the subsequent refinement stage, reinforcing our selection of 0.9m robust baseline.
E SCORING FUNCTION IMPLEMENTATION DETAILS
This appendix provides the detailed composition of the scoring functions introduced in the main text. Our evaluation framework is designed to be comprehensive, balancing hard safety constraints with continuous measures of driving quality and efficiency.
The final score for a trajectory, which underpins our Sylobal and Sheal scorers, is computed as a product of Hard Safety Compliance term (H()) and Performance Quality term (Q(7)).
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Table 8: Ablation on Goal Proposal Parameters (GCG Stage Only).
(a) Goal Points (K)
1
3 (Def)
5
10
PDMS
84.1
87.4
88.7
87.6
(b) NMS Threshold (dsms)
Thresh. (m)
0.0
0.3
0.9 (Def)
1.2
PDMS
85.8
86.5
87.4
88.0
Figure 7: Qualitative Visualization of Goal Candidates (K 1 K 3). The Black trajec- tories represent the Top-1 choice (equivalent to K = 1), which fails in these challenging scenarios. The Red trajectories represent the Top-2 and Top-3 candidates introduced by setting K In these cases, the alternative red trajectories successfully avoid obstacles or boundaries, demonstrat- ing how multimodal proposals improve robustness.
E. HARD SAFETY COMPLIANCE TERM (H(T))
This term acts as a safety gatekeeper. It is the product of several individual metric scores, each corresponding to an inviolable driving nule. If any rule broken, this entire term approaches zero, effectively nullifying the trajectory's 's score regardless of its performance quality.
H(T) mxc(T) mDxc(T)
(5)
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Figure 8: Visualization of NMS Threshold Effects (dxMs = 0.9m vs. 2.1m). The top row displays candidates generated with the default threshold (0.9m). exhibiting tighter clustering. The bottom row shows the same scenarios with a larger threshold (2.1m), which forces greater spatial separation between candidates.
The individual metries are defined follows:
This metric penalizes collisions for which the ego vehicle is deemed responsible. A collision is considered "at-fault" if the ego vehicle's front collides with any object, or if collides with a static object.
mNc (No at-fault Collision):
Score 1.0: No at-fault collision occurs.
Score 0.5: An at-fault collision with a static object occurs.
Score 0.0: Any other at-fault collision occurs.
mDAc (Drivable Area Compliance): This is strict binary metric that ensures the vehicle remains within the legally designated drivable area.
Score 1.0: The vehicle's entire footprint remains within the drivable area.
Score 0.0: Any part of the vehicle's 's footprint goes outside the drivable area.
Our Safety Scorer (Sane) uses this exact logic, evaluating these hard constraints at each waypoint to detect failures.
E.2 PERFORMANCE QUALITY TERM (Q(T))
This term evaluates the quality of a trajectory that has passed the hard safety checks. It is normal- ized weighted sum of several performance metrics.
wpp (pp(T) + wTTc MTTC(T) + we mc(r)
Q(T)
(6)
WEr + wrre + we
The individual metries and their weights are as follows:
mgr (Ego Progress): This metric measures the vehicle's progress along its intended high- level route. The value is normalized to a range of [0, 1] based on a feasible upper bound for progress in the given scene.
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Weight (WEP):
mrrc TTime-to-Collision This metric ensures safe temporal buffer to other agents. It is binary score based a predefined safety threshold.
Score 1.0: The minimum TTC to any other agent remains above the safe threshold
(e.g.. 2.0 seconds).
-Score = 0.0: The minimum TTC drops below the threshold.
-Weight (wrrc):
me (Comfort): This metric evaluates ride smoothness. It is binary score based on whether the vehicle's dynamies stay within acceptable bounds.
Score 1.0: Longitudinal and lateral acceleration and jerk all remain within prede-
fined comfort limits.
Score = 0.0: Any of the dynamic limits are exceeded.
Weight (we): 2
F SCALABILITY AND COMPARISON WITH CONTINUOUS DIFFUSION
To further validate the effectiveness and scalability of our discrete diffusion framework, we con- ducted a large-scale open-loop evaluation using an in-house dataset comprising approximately billion samples. This dataset offers comprehensive coverage of diverse and complex driving scenar- ios, far exceeding the scale of public benchmarks.
We compared our discrete diffusion VLA against continuous diffusion VLA baseline trained on the same data. Both models generated 8 trajectories per sample. We report the Average Displacement Error (ADE) and Final Displacement Error (FDE) at 40m, 80m, and 120m horizons. Top/ refers to the trajectory with the highest predicted confidence, while Min refers to the best trajectory among the 8 samples (oracle selection).
As shown in Table 9, the discrete diffusion VLA consistently outperforms the continuous diffu- sion bascline across most metrics, particularly in long-horizon prediction (120m FDE: 2.19 vs. 2.71). This empirical evidence suggests that discrete tokenization effectively captures complex multi-modal distributions at scale, validating it as robust alternative to continuous parameterization for autonomous driving planning.
Table 9: Open-Loop Comparison on Large-Scall Scale in-House Dataset (IB Samples).
Top1 FDE
Top1 ADE
Min FDE
Min ADE
Method
40m
80m
120m
40m
80m
120m
40m
80m
120m
40m
80m
120m
Continuous Diff. VLA
0.72
1.53
2.71
0.35
0.74
1.00
0.35
0.76
1.44
0.21
0.44
0.61
Discrete Diff. VLA
0.67
1.37
2.19
0.34
0.69
1.02
0.29
0.68
1.06
0.17
0.39
0.59
G LIMITATIONS & FUTURE WORK
Here, we discuss our limitaitons and interesting future works.
Model Inputs. Our method relies on three-view images of the current frame as input. Never- theless, single-frame frame images fail to capture velocity information, leaving the motion directions and speeds of surrounding vehicles unknown. Only by incorporating historical images and additional rich information input can the model's interaction capabilities be fully utilized.
Solution and future work: can incorporate historical images to enable the model to output not only planned trajectories but also the trajectories of key obstacles, providing a foundation for the reward model and subsequent trajectory game-theoretic interactions.
First, CondiConditioned Generation should primarily focus high-level objectives such as navigation compliance and traffic efficiency. In practical applications, scoring should prior- itize these aspects. For rapid validation in this work, we directly adopted the PDM scorer without
Reflection.
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task-specific adjustments. Second, in terms of Safety- Guitee Regeneration, both the number of iterations and online inference attempts affect the final outcomes. While achieving better results requires sacrificing inference time, experimental findings indicate that more inference opportu- nities do not necessarily yield better performance. Our analysis of failure cases reveals the following insights, shown in Figure 6:
Oscillation Between Boundaries: The model tends to oscillate between boundary violations and collision avoidance in its final reasoning, particularly in scenarios with limited drivable space. This likely stems from increased difficulty caused by inherent errors in discrete trajectory representation. Future work could explore alternative methods mitigate this issue.
2. Navigation Correctness: The reward function does not account for navigation correctness, leading incorrect correction directions in certain scenarios. This can be addressed through iterative reward function refinement.
Goal Point Selection: Suboptimal goal point performance in specific scenarios limits correction capability when the search range is constrained. This could be improved by enhancing the base model through reinforcement learning or other advanced techniques.
Solution and future work: We can replace the rule-based reward with a model-based reward, and the search process can also be internalized within the model to some extent for reward- guided reflection, though this may introduce corner cases in certain scenarios.
Sample Efficiency. Since the primary focus of this work is on method validation, we have not invested significant effort in algorithm optimization and acceleration, leaving substantial room for improvement.
Solution and futuree work: Since the output token count is relatively small, more inference iterations do not necessarily yield better results, and this could be reduced in future work. Additionally, engi- neering optimizations such as KV cache can be implemented to improve computational efficiency.
Overall, although some design choices may appear simple and certain limitations exist, we have thoroughly demonstrated the capabilities of ReflectDrive models for closed-loop planning in au- tonomous driving through extensive experiments. Moreover, we demonstrate the potential of Re- flectDrive model to provide safety driving behavior. It provides nigh-perforrmncc, highly adapt- able planner for autonomous driving systems.
THE USE OF LARGE LANGUAGE MODELS (LLMs)
LLMs were used exclusively as writing assistance tools in preparing this manuscript. Specifically, we employed LLMs for grammar checking. All research ideation, experimental design, analysis, and scientific conclusions are entirely the work of the authors. The LLMs played no role in the conception of research questions, methodology development, or interpretation of results. Authors take full responsibility for all content in this paper, including any text refined with LLM assistance.
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Reviews:
[{"review_id": "1e5WX6KrXz", "reviewer": "Reviewer_qxE7", "summary": "This paper proposes ReflectDrive, a framework for end-to-end autonomous driving that leverages a Vision-Language-Action (VLA) model with a discrete diffusion planner. The core contributions are: 1) The discretization of the continuous driving space into an action codebook, which allows for the use of a discrete diffusion model for trajectory generation. 2) A reflection mechanism with a gradient-free, iterative inference-time process. This mechanism first generates multi-modal trajectories based on sampled goals and then uses a safety-guided regeneration step to correct unsafe waypoints identified by a safety scorer.", "questions": "1. What do the variables c and s represent in Equation (2)?\n2. The safety scoring mechanism described in Appendix C (e.g., $m_{DAC}$, $m_{NC}$) seems to require ground-truth or privileged simulation information. How do you envision this safety-guided regeneration being applied in a real-world vehicle that relies only on on-board sensor data?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "1. The application of discrete diffusion and an inpainting-based reflection mechanism to a VLA model for driving is a novel approach to integrating safety constraints.\n2. The work directly tackles the critical and unsolved problem of enforcing hard safety constraints (like collision avoidance and drivable area compliance) within imitation learning-based frameworks.", "weaknesses": "1. The safety score is a very dedicated design that still falls into the rule-based refinement. More critically, obtaining these scores would inevitably employ some privileged information. For example, the DAC score will require the knowledge of whether the car will fall out of the drivable region, which is impossible to reliably obtain purely from sensor data in the real world. This reliance on an oracle-like safety scorer questions the practical applicability of the reflection mechanism. It will be nice to see ablation studies where some less reliable, learning-based rewards are involved.\n2. To justify the effectiveness of the discrete action space, it would be beneficial to see an ablation study on different granularities of the discretization.", "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": "yYrPCyJv7r", "reviewer": "Reviewer_pLbU", "summary": "The paper presents ReflectDrive, a novel framework that integrates a reflection mechanism for safe trajectory generation in autonomous driving using discrete diffusion models. The authors aim to enhance the safety and reliability of trajectory planning by combining discrete diffusion with goal-based trajectory planning. The framework introduces three scoring functions—Global Scorer, Safety Scorer, and Local Scorer—to evaluate the generated trajectories and ensure adherence to safety constraints.", "questions": "which scorer play the most important role? Is it possible to use single scorer?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. **Safety Focus**: The emphasis on safety through the reflection mechanism and scoring functions demonstrates a commitment to developing reliable autonomous systems.\n2. **Evaluation on Closed-loop Benchmarks**: The use of NAVISM benchmark for evaluation adds credibility to the findings and demonstrates practical applicability.\n3. **Easy to follow**: The key idea is clear and the paper is easy to understand.", "weaknesses": "1. **Limited Novelty**: The primary contribution of the paper appears to be the combination of discrete diffusion models with goal-based trajectory planning. This concept closely resembles the existing GoalFlow work, where flow matching is replaced by discrete diffusion, and more scorers are used. The incremental nature of this contribution raises questions about its originality.\n   \n2. **Insufficient Ablation Studies**: While the paper proposes three distinct scoring functions, comprehensive ablation studies are lacking. Only two of the scorers are evaluated, which does not provide a complete understanding of the impact of each component on the overall performance. A thorough ablation analysis is necessary to validate the effectiveness of all proposed components.\n\n3. **Lack of Detail in Safety-Guided Regeneration**: The section on Safety-Guided Regeneration is overly simplistic. It does not adequately address the success rate of the local search strategy or provide contingency plans for search failures. Additionally, the generalizability of this search method in extreme or unusual scenarios is not explored, which is critical for assessing its robustness.", "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": "hc1ZWMXYuC", "reviewer": "Reviewer_dnw7", "summary": "The paper proposes ReflectDrive, a VLA-based end-to-end planner that (1) discretizes 2D waypoints into a token codebook; (2) uses a discrete diffusion (masked-LM–style) model to inpaint trajectories; and (3) adds a reflection loop at inference: detect unsafe tokens via a safety scorer, do local discrete search to find a “safety anchor,” then re-inpaint around it—no gradients needed. Evaluated on NAVSIM with PDMS metrics (NC, DAC, TTC, Comfort, EP), the reflective loop substantially improves safety and progress, and with oracle agents it approaches human PDMS.", "questions": "1. Codebook & search: What Δg and codebook size |A| do you use? How is δ chosen (and its runtime impact)? Any adaptive δ strategy during reflection?\n2. Noise schedule: You mention cosine-like masking, S=5 inference steps. What is the exact masking schedule during training/inference (token-wise remask policy, “remask low-confidence” threshold)?\n3. Please formalize how “at-fault” collisions are computed and how TTC is thresholded; include ablations of scorer weights, W_ep, W_ttc, W_c.\n4. Goal proposal: Why K=3 and d_NMS=0.9 m? Show sensitivity (K=1/3/5/10 already partly given—add per-metric trends and qualitative examples).\n5. Variance & failures: In the oscillation failures (boundary vs collision), did larger δ or more iterations fix them, or is it a structural issue of discrete grids? Any hybrid continuous snap-back after the discrete anchor?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Pros:\n\n1. **Clear, modular idea**: discrete tokenization → diffusion inpainting → gradient-free reflective editing. Nicely matches the structure of discrete diffusion (mask/remask/inpaint) and makes constraint injection tractable at inference. \n2. **Practical safety loop**: the local Manhattan search over tokens + re-inpainting is simple and fast, with most cases finishing in 1–3 reflection iterations. With five diffusion steps, remask-low-confidence decoding, K=3 goals, NMS=0.9 m, max 10 refinement steps. Concrete enough to re-implement.\n3. **Ablations and attribution**: Tables in the experiments section isolate contributions of goal-conditioning vs safety-guided regeneration;", "weaknesses": "Cons: (There are no obviously novel concerns, but some questions)\n1. A core theme of the paper is safety and rules. From the ablation in Table 2, Goal-Conditioned Generation (GCG) and Safety-Guided Regeneration (SGR) have the larger impact. So I infer that the “safety” you highlight mainly comes from this reflective module. If so, on the one hand, where does Discrete Diffusion deliver unique value? On the other hand, did you try other trajectory parameterizations combined with goal and safety-anchor guidance?\n2. There is a lack of description of the training data for multi-modal behavioral modes. I’m curious how the training data influences behavioral multi-modality: if the data is concentrated on a small set of behaviors (i.e., collected from only a few drivers’ styles), how do you train a top-k set of diverse behaviors? How do you avoid trajectory mode collapse?\n3. There is a lack of description of the training data for multi-modal behavioral modes. I’m curious how the training data influences behavioral multi-modality: if the data is concentrated on a small set of behaviors (i.e., collected from only a few drivers’ styles), how do you train a top-k set of diverse behaviors? How do you avoid trajectory mode collapse?\n4. In the Introduction, the paper emphasizes that guidance needs gradients, leading to slow sampling, hyper-parameter sensitivity, and potential instability. It repeatedly claims one benefit of discrete diffusion is eliminating gradient computation to reduce cost, and around line 60 states that other deployment schemes suffer slow sampling due to gradients. However, generation-based planning also depends on the number of sampling steps and the stochastic process; you use step-skipping with only 5 steps, yet the paper appears to lack experimental analysis on computational cost and speed, while stressing that “discretization enables efficient search.” The Limitations section explicitly notes that no algorithmic/engineering acceleration was applied and there is still room for efficiency improvement. Additionally, around line 203 the paper says accuracy is affected by discretization resolution; as resolution increases to meet accuracy/quality, inference speed will inevitably be impacted, and that speed is tightly coupled with the large model’s forward pass. I hope the authors can provide analysis and results validating the significance of discrete diffusion, the inference speed, and the effect of resolution.\n5.  The paper calls NAVSIM a “real-world autonomous driving benchmark,” but NAVSIM is a data-driven simulator; that nuance matters for claims of real-world safety. Please clarify wording and discuss implications (non-reactive agents, sim-to-real).\n 6. Some baselines use Camera+LiDAR (e.g., Transfuser, Hydra-MDP, DiffusionDrive/GoalFlow) whereas ReflectDrive is camera-only in the main line. Mixed-modality comparisons are informative but not apples-to-apples; add camera-only versions of diffusion/flow baselines or provide a C&L variant of ReflectDrive.\n7. Safety oracle realism. The reflection loop’s default scorer assumes constant-velocity surrounding agents, which the authors themselves link to sub-par TTC/NC vs the oracle variant; this can bias safety judgments during reflection. Please add a learned (or stronger rule-based) predictor to close the realism gap.\n8. Reward/metric shaping risks. The Global/Local scorers combine hard safety gates with weighted PDMS-like terms; optimizing a test-metric proxy at inference risks over-fitting to evaluator quirks (e.g., binary TTC thresholding). Provide robustness checks or alternative scorers.\n9. Missing discretization specifics. The core idea hinges on codebook resolution (Δg), grid size |A|, and search radius δ. The paper states spatial range  [−100,100 ], but not Δg nor the token vocabulary size actually used at inference; please report these and study sensitivity.\n10. Latency and systems profile.  The method is “designed for real-time,” yet no ms/frame or hardware stats are given (diffusion steps + reflection iterations + scorer costs). Please provide latency/throughput/memory on a target GPU/SoC and discuss worst-case bounds.", "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)