Aux-Think: Exploring Reasoning Strategies for Data-Efficient Vision-Language Navigation
Authors: Shuo Wang
Yongcai Wang
Wanting Li
Xudong Cai
Yucheng Wang
Maiyue Chen
kaihui.wang
Zhizhong Su
Deying Li
Zhaoxin Fan
Venue: NeurIPS 2025 poster ()
Keywords: vision-language navigation
chain of thought
data efficiency
Primary area: applications

Abstract: Vision-Language Navigation is a critical task for developing embodied agents that can follow natural language instructions to navigate in complex real-world environments.  Recent advances by finetuning large pretrained models have significantly improved generalization and instruction grounding compared to traditional approaches. However, the role of reasoning strategies in navigation—an action-centric, long-horizon task—remains underexplored, despite Chain-of-Thought reasoning's demonstrated success in static tasks like question answering and visual reasoning. To address this gap, we conduct the first systematic evaluation of reasoning strategies for VLN, including No-Think (direct action prediction), Pre-Think (reason before action), and Post-Think (reason after action). Surprisingly, our findings reveal the Inference-time Reasoning Collaps issue, where inference-time reasoning degrades navigation accuracy, highlighting the challenges of integrating reasoning into VLN. Based on this insight, we propose Aux-Think, a framework that trains models to internalize structured reasoning patterns through CoT supervision during training, while preserving No-Think inference for efficient action prediction. To support this framework, we release R2R-CoT-320k, a large-scale Chain-of-Thought annotated dataset.  Empirically, Aux-Think significantly reduces training effort without compromising performance.

Aux-Think: Exploring Reasoning Strategies for Data-Efficient Vision-Language Navigation
Shuo Wang Yongcai Wang'! Wanting Li Xudong Cai', Yucheng Wang Maiyue Chen", Kaihui Wang", Zhizhong Su Deying Li Zhaoxin Fan
Renmiin University of China, "Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing. Horizon Robotics
https:/horizonrootiics wwtp:tttis...iiiii.. github. wwtrooottreeeeeee lab/ lab/aux- aux think
Aus-Th (Oun))
ROVLA
50
NSMLA
Kovid
Aus-Think
Success cess Pats
30.8
30.2
Uni Navidl
Korld
15.9
12.8
havis
SR
SPL
Metrics
Taining
(a) Comparison of navigation performance of different reasoning strategies, which are only trained D2R-CoT-322 320k without receding- horizon action planning. The proposed Aux- Think (Ours) method consistently outper- forms other reasoning strategies.
(b) Aux- Thx-Think (Ours) is Parcto-optimal
data efficiency and success rate. Variants: Aux-Think' only trained on R2R-CoT-320K Navid DAgger [ and instruction data.
Uni-Navidf: no VQA data. NaVILAţ: no Internet data.
Figure/ 1:
Aux-Think outperforms alternative reasoning approaches in navigation tasks (a) and achieves a favorable trade-off between data usage and success rate (b).
Abstract
Vision-Language Navigation (VLN) is a critical task for developing embodied agents that can follow natural language instructions to navigate in complex real- world environments. Recent advances driven by large pretrained models have significantly improved generalization and instruction grounding compared tradi- tional approaches. However, reasoning strategies in this task remain underexplored. Navigation is action-centric and long- horizon, while Chain-of-Tie -fhhuughhh (CoT) rea- soning has mainly shown success in static tasks such visual question answering. To address this gap. we conduct the first systematic evaluation of reasoning strate- gies, including No-Think (direct action prediction), Pre-Think (reasoning before action), and Post- Post-Think (reasoning after action). Surprisingly, our findings reveal Test-time Reasoning Collapse issue, where reasoning during testing degrades navigation accuracy, highlighting the challenges of integrating reasoning into em- bodied navigation. Based this insight, we propose Aux-Think, framework
"This work done while Shuo Wang Research Intern with Horizon Robotics. 1 Corresponding authors. PProject leader.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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that trains models to internalize structured reasoning patterns via CoT supervision, while predicting actions directly without explicit reasoning at test time. To support this framework, we release R2R-CoT-320K,, the first Chain-of- Chain-of-Thought annotated dataset for VI.. Extensive experiments show that Aux-Think substantially reduces training effort and achieves ttateoo--the-ae performance on success rate.
Introduction
represents a groundbreaking step towards enabling robots to understand natural language instructions and navigate complex, unfamiliar environments. As a foundational capability for embodied AI systems, VLN bridges the gap between perception and action, empowering robots to seamlessly interact with the real world. In particular, Vision Vision-Language Navigation in continuous environments (VLN-CE) [5] 6 7 has emerged as a critical research focus, pushing the boundaries of autonomy and adaptability in dynamic, real-world scenarios.
Aiioo-and-Languaaee Navigation (VLN) 2 R
Traditional Vision-and-Lani Lnd-Lang Navigation methods often rely on waypoint predictors topology maps [TO, [1 12 13 but struggle with generalization and the sim-to-real gap. With the advancements in Large Language Models (LLMs) [14 15] and Vision-Language Models (VLMs)
9 or
16 17 [8] recent studies [19 20 21 shift toward action prediction via supervised fine-tuning on paired videos and instructions. Despite these advancements, most efforts emphasize training strategies [2] [23], data organization [24), model architecture (25) for VLN. Chain-of-Thougeee
(CoT), which explicitly generates intermediate reasoning before producing final answers [26], has shown success in enhancing reasoning capabilities across various LLM- and VLM-driven tasks, like
video understanding [27 and tool usage 28 However, its application to VLN remains unexplored.
Motivated by this gap, we present the first systematic study of reasoning strategies in VLN, comparing three paradigms: (1) No-Think, which predicts actions without explicit reasoning: (2) Pre-Think (29). which performs CoT before action selection; and (3) Post-Think (30), which reasons after action prediction. Our key findings reveal a phenomenon we term "Test-time Reasoning Collapse" (TRC): Introducing CoT via Pre-Think or Post-Thiikk consistently harms navigation performance (Fig. Tap. CoT errors or hallucinations during testing result in incorrect actions, as shown in Fig. B We attribute this to craining-testing mismatch: CoT is only trained on optimal (oracle) trajectories, but for testing, agents often enter unfamiliar, off-distribution states where reasoning fails, leading to error accumulation along the trajectory and cascading navigation failures. Even with DAgger, coverage of off-distribution states is limited and CoT supervision remains biased toward the optimal region. This phenomenon highlights fundamental limitation of multi-turn explicit reasoning in dynamic, partially observable environments, unlike the single-turn static tasks such as VQA or image captioning 31 (2).
To address the TRC issue, we propose Aux-Think, inspired by the dual-process theory of human learning (33): during training, humans often rely explicit reasoning to understand principles, but during exccution, they focus on actions without consciously recalling those principles, much like driver who no longer recites traffic rules while driving. Similarly, Aux-Thini uses CoT as auxiliary signal during training to guide the model in internalizing reasoning patterns. At testing time, the model no longer generates explicit reasoning, but instead directly predicts actions based on the internalized reasoning learned during training. This separation between learning and execution improves decision focus, reduces testing overhead and hallucinations, and leads to more accurate and stable navigation.
Specifically, in Aux-Think, the generation of CoT reasoning and navigation actions is decoupled into two distinct tasks during training: (1) generating navigation actions based on stepwise observations and language instructions the primary task, and (2) generating the reasoning process for each step as an auxiliary task. By leveraging prompts to switch between these tasks, we independently supervise navigation actions and reasoning processes, effectively avoiding the negative interference caused by jointly training both tasks. During testing, Aux- Think directly predicts actions without intermediate reasoning, thereby eliminating the risk of errors introduced by CoT hallucinations.
To validate the effectiveness of Aux-Think, we introduce R2R- -2R-CoT-320k, the first CoT dataset for VIN, which is large-scale and specifically tailored for the R2R-CE benchmark 34 As existing datasets lack aligned CoT-style reasoning, we construct this dataset by generating reasoning traces that can faithfully lead to the correct next action. R2R-CoT-322 320k consists of over 320,000 diverse

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reasoning traces grounded in natural instructions and photo-realistic navigation trajectories, It covers wide range of scenarios and CoT content, making it rich and challenging resource for training and evaluation. We show that Aux-Think, when trained with R2R-CoT- CRR-CoT-320k, matches the performance of state-of-the-ar VLN methods, while using only fraction of their training data (Fig. lb).
Our contributions are as follows:
We conduct systematic comparison reasoning strategies in VLN and reveal that test-time reasoning, including Pre-Think and Think,, consistently underper- forms direct action prediction (No-Think), termed the TRC issue. To knowledge, this is the first exploration of CoT strategies the VLN-CE task.
New Finding:
We propose Aux-Think, a novel training paradigm that uses CoT as auxiliary supervision while maintaining No-Think testing, achieving superior performance other reasoning methods. Aux-Think pioneers a new perspective on CoT utilization and achieves the best performance the navigation success rate.
New Method:
New Dataset: We introduce R2R-CoT-3201 of-Thought dataset tailored for the R2R-CE benchmark, which enables more effective training of reasoning-aware VLN agents.
large-scale, diverse, and challenging Chain-
Action
CoT
W
Action
Action
CoT
VLA
VLA
VLA
Observation
Instruction
Observation
Instruction
Observation
Instruction
(a) No-Think
(b) Pre-Think
(c) Post-Think
Seceding-Horizonn Action Planning
CoT-based Reasoning (Training Only)
Instruction- Reasoning (Training Only)
Obs. Taken
Token
Nx
Action
The viteo provteed shoue path
doougn comert image
nignedde
CoT
frruugh the dear and head fonwel the dhan aut
Instruction
postion
the d9' Aeection
VLA
Observation
Instruction
Observation
Instruction
Observation
(d) Aux-Think
Figure 2: Illustration of Lux-Think and other reasoning strategies, Unlike No-Think, Pre-Teeee Think, and Post-Think, our Aux- -hink introduces auxiliary CoT- and instruction- based reasoning during training while maintaining efficient action planning at testing.
2 Related Work
2.1 Traditional VLN Methods
Before the emergence of large-scale pretrained models, VLN methods are primarily built modular pipelines trained via imitation [35 36 37 or reinforcement learning [38] 39], often with handcrafted visual features and auxiliary objectives such as progress monitoring or instruction reweighting. These models typically operate with panoramic observations and discretized actions, as in benchmarks like Room-to-F RooRo (R2R) (3] and Room-Across- Room (RxR) 40) The traditional auxiliary reasoning tasks have also been explored, which rely specific network outputs to predict structured, low-level reasoning results like task progress or trajectory alignment. More recent work has explored navigation
in continuous action spaces using egocentric visual inputs [34 41, which is the setting adopted in

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Cross and go through brown doors, step the nearest archway on right, step near glass stop.
Histonical Observations
Current Observation
Pre-Think (Reasoning Before Action)
<thiskoLet me think stop by step. The instruction says comoon The Natoricl observations show visua trajectory moving through living room The current osservation alt ows moving through lving room and seems that we have not crassed the room. To complese the first stop, must keep going staight, which seomed to further the requirements of the instructions </think> canswor-The next oct move forward 75cm <aanswer>
Hatorkal
Fulure Trajecory
Post-Think (Reasoning After Action)
<answarr-hh nex action is move forward 75mm, <lanswer> athinkoLat me think about it canefuly.. The salk la to -.o.een But upon revlawing the given maaes, they con't diep ay the archway. The goal to aross the room first and continue moving, the task seoms mismarch the current ossorvations mis.u. Ch. Isce! There no obvious coors or any archway in the current the past ooservations Since nned to continue my task, keep moving forward for more information should be good Idea. <thhink
Positton
Aux-Think (Auxiliary Reasoning and Direct Action)
Cument Poaiton
Lot Given the instruction and the images. need the action brown doors and the found
observations
Next. defermine whether riggtt 15 degrees.
Target Fosstio
first the time.
impossiblle
explore
Figure 3: Illustration of CoT and Action Prediction Results Using Different Reasoning Strategies. Pre- Think generates incorrect actions (e.g.. "move forward 75cm") due to flawed CoT reasoning, such as "we have not crossed the room,"" leading to significant trajectory deviation. Post-Think, which builds on Pre-Think's ss output, inherits similar reasoning errors (e.g.. "no obvious door or archway") and makes the same wrong prediction. In contrast, Aux-Think correctly predicts "turn right 15 degrees" and follows a trajectory aligned with the ground truth. While Aux-Thint does not rely on CoT during testing, it can optionally produce CoT via prompt switching- its action prediction remains accurate even when the generated CoT is of moderate quality. This highlights Aux-Think's robustness to imperfect reasoning and its superior reliability in action prediction.
study, Our method replaces handcrafted pipelines with vision-language models that directly predict agent actions, aiming to improve instruction following in realistic environments.
2.2
VLN with Large Pretrained Models
Recent advancements have seen the integration of large pre- pre-trained models [17
16 14 42). into VLN tasks, Early explorations of LLM in the VLN field usually use off- off-the-shelf large language models to select landmarks or waypoints in a zero-shot manner 443 44 45 41. Recent works have focused on model. Notably, Poliformer 23 and Na Vid 19 introduce video-based monocular VIN, demonstrating navigation capabillitess using monocular RGB videos without maps or depth input. Vid 20 unifies various navigation tasks, including VLN, ObjectNav [46], Embodied Question Answering [47, and Human- following 48 49. into single model trained on diverse dataset. NaVILA 21 further extends this approach by integrating VLN with legged robot locomotion skills in complex environments.
fine-tuning the VLM to obtain the navigational Vision LariionLanggagg-Atttto
While these models have improved the alignment among visual understanding, language instructions. and navigation actions, they predominantly employ No-Think testing strategies, lacking reasoning mechanisms. Moreover, their performance gains often stem from leveraging extensive datasets, whereas our approach focuses on exploring reasoning strategies.
2.3 Reasoning Models
Recent advances like Chain-of-Thie Thought (CoT) 26 ReAct [50), and Toolformer [51 highlight the
potential of LLMs to perform explicit reasoning in static and multimodal tasks, including VQA [52), visual grounding [53], and video understanding [27), where Pre-Think strategies have shown success. Similar ideas have been explored in embodied tasks like manipulation [54) and control
[55]. However, a recent study 30) argues that small models may benefit more from No- No-Think or
Post- Think strategies due to limited CoT quality.

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In our work, we conduct the first systematic comparison of reasoning strategies for VLN. Based on our findings, we propose Aux-Think, a novel framework that leverages CoT' reasoning as auxiliary supervision during training while maintaining No-Think testing, thereby enhancing data efficiency and performance in VLN.
and No-Think
3 Method
3.1 Problem Setup
We study monocular iaiion-and-Lanuuaad Navigation in continuous environments (VLN-CE), where an embodied agent navigates photo-realistic indoor environments by following natural language instructions. VLN-CE emphasizes generalization to unseen environments and supports both forward and reverse navigation, offering a comprehensive test of spatial reasoning and language grounding.
At each time step, the agent receives: (1) a natural language instruction, typically a short paragraph specifying the navigation goal: (2) a RGB observation from the agent's current viewpoint; and (3) historical observations, including 8 frames uniformly sampled from all historical frames (always including the first frame). The agent selects an action (e.g., move forward, turn left/right by specific degree, or stop). The objective is to generate an action sequence that follows the instruction as accurately and efficiently as possible until the agent reaches the target position.
Within the Supervised Fine-Tuning (SFT) framework, our VLN model based on NVILA 8B [17, is learned by imitating expert demonstrations from the dataset, where each trajectory provides sequences of <navigation context, expert action> pairs, with navigation context denoting the combination of historical observations, the current observation, and the instruction.
3.2 R2R-CoT-320k Dataset Construction
We present R2R-CoT-320k, the first VLN dataset annotated with CoT reasoning, tailored for the R2R-CE benchmark. We reconstruct step-wise navigation trajectories in the Habitat simulator [56]. Each sample the dataset comprises the current view, the historical visual context, the corresponding instruction, and the ground-trum action. We employ Qwen-2.5-VL-77 publicly available VLMs, generate detailed CoT for each navigation sample (Fig. 4. For Pre- Think
[16). one of the strongest
and Post-Think strategies, we format reasoning traces with <think><<think> and <answer></answer> tags, following recent reasoning models [77]
See Appendix A.2 2 for more details on our dataset.
Observations
Current Observation
Text Prompt
you mieol for movigation tasks. You have been givan Nistorical and curment ocservetion Your ad taak is:
Pass the fireplace and straight out the door ahead. Walt by the chairs that have flowers The final answer is: The next answer tumn right 15 degrees.
Please think about this
pandering deeply: Engage intemal
dialogue using expressions such as let think, watt, 'Hmmn, 'sh, soe', tet's break it. down', etc. or other natural language thought expressions, It's encouraged induue seff reflection wiffistionn the resisoning preceise Provide detailed ressoning.
Large-scale VLM Annotator
CoT Annotation
Lot mo thini.. the task hand is to navigate through the observed environment to reach specifi goot: passing the froplace, then going straigtt out the door sheed. finely weiling the thars with foownss It's cricial anelyn each image and underalied the leynit, orentation, and movemve necassary achreve the took Bosk.Pro From the sequence. the first imag shows doose-u up view of the freplace and a coor. Nexi, the second image soems to give a broader view are deain a langer context with way kacking hallway with coors on both sidees entiazing a sense moving forward towards a spactic area Finally the mith image shows room with fireptace and the doonway leading out the presituus aequence imples that the chairs with flowrrs ars in this rom Innitiei. ssems the cumeett observation aireecy sequence and the desoriation the However, based on nineleenth image: VnlaHimm, let' break dooo. The cheins fooers are dearly fowers on cheirs. leee... considuring the final action should be
where Troplace
The through siglth imagas show progression throagh
desired according
provided, goal wasn't achioved
reached the
chaics with the Dowers
seen proparty the overhisd
visibla bwalth image: cument observation friss the presence right to degrees"" to ensure that the robot property aligned towards the chairs and the spectied gool.
Figure 4: The annotation pipeline of our R2R-CoT-320k dataset.
5

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3.3 Systematic Investigation on Reasoning Strategies for VLN
To investigate the impact of reasoning on VLN, we study and evaluate three distinct strategies for integrating Chain-of-Thought (CoT) reasoning during training and testing.
No-Think: The agent directly predicts the next action based on the current observation and instruction, without any intermediate reasoning.
CaT Weight
Pre-Think:
The agent first generates explicit reasoning trace based on the instruction and current observation. The following predicted actions are conditioned on the CoT output.
50
Rate
20
The agent first predicts action and then retrospectively generates a reasoning trace that explains the decision.
Post-Think:
The training loss for the VIN model my with above three strategies is:
Pro Think
RostThnk
No-Think
Figure 5: Comparision of success rate
Pre-Think, Post-Think, and No-Think.
T
log (aj Or: I7) log No < c, az > 10,, or, I+) log np( a7, q > 10 or. I+)
No-Think
I(0) = -
for Pre-Think
TED ==
for Post-Thine
where D is the training dataset; T' the total number of timesteps in trajectory т, and & is the current timestep. af is the ground truth action and ot is the ground truth reasoning trace. O, represents the history of observations up timestep or the current visual observation timestep I, and I, is the natural language instruction provided to the agent.
We isolate the impact of reasoning overall navigation performance. In addition, adjust the loss weight the CoT part (cf in Equation Th to make the model more focused on learning the action Our key findings include:
(Fig. 5)
and Post-Think perform significantly worse than No-Think. Despite involving explicit reasoning, both strategies lead to lower navigation success and efficiency, highlighting the unreliability of test-time CoT in dynamic environments.
Finding 1:
Finding 2: Careful Balancing of Explicit CoT Loss Weight Improves Performance. By carefully tuning the explicit CoT weight for Pre- ThiTThink and Post-Think, we find that balanced supervision yields slight gains, indicating that training emphasis reasoning is key, strategy-dependen factor despite test-time unreliability.
We further analyze the performance degradation observed with Pre-Think and ThinkThink strategies. During training. the model only exposed to optimal sequences of states and actions in oracle trajectories. However, VLN environments inherently complex, dynamic, and partially observable, increasing the likelihood of agents deviating from optimal, oracle-guided training trajec- tories. Consequently, when encountering non-oracle, out-of-distribution states during testing, the CoT reasoning generated by these strategies is susceptible to drift, potentially yielding inaccurate or hallucinated interpretations of the environment and instructions.
In Pre-Think, the model's actions rely heavily on the preceding reasoning chain, making decisions fragile. Any hallucination or misstep in reasoning directly leads to wrong actions. In although actions are generated first, the need to produce follow-up explanations still alters the model's hidden states. This subtle interference, like reserving capacity shifting attention, compromise the quality of the initial action decision. A detailed example provided in Fig.
Moreover, agents perform multi-step action prediction, where an error any intermediate step further pushes the agent away from the correct state distribution. This compounding effect leads to cascading errors in both CoT reasoning and subsequent actions, ultimately resulting in trajectory- failures.

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3.4 Aux-Think: Reasoning-Aware Co-Training Strategy
To address the challenges from the explicit CoT training to VLN, we propose reasoning strategy designed to enhance navigation performance without incurring test-time problems. Aux-Think leverages reasoning exclusively during training through auxiliary tasks. We design two reasoning-based auxiliary tasks and one action-based primary task during training.
novel
CoT--aaeed Reasoning. The model is trained generate CoT traces conditioned the given instruction I, current observation Og, and historical observations O, This encourages the acquisition of structured reasoning patterns and strengthens the connection between language, vision, and actions. The loss of CoT-based reasoning for each trajectory is:
COT (0)
Σ log no (c, Oi, It, t=
where of is the ground-truth reasoning trace at step f.
Instruction-based Reasoning. Given sequence of visual observations Σ Oz the model is trained to reconstruct the corresponding instruction J. This reverse reasoning task provides complementary
supervision beyond CoT-based signals, further enriching the model's semantic grounding. The training loss is:
T
L. Ins (0) = -ol To (T, Σ o,)
(3)
t=
sereding-Horizonn Action Planning. We introduce Receding Horizon Action Planning pri- mary task. During training, the model predicts a sequence the next n actions (a,, a,+1.... a(+n-1) based the instruction current observation ot, and navigation history O₂ for the sample time step f. This setup encourages short-term forecasting while retaining reactivity to new observations. The training objective for each trajectory is defined
T.
L. Let = ΣΣ log ne
vo (ai+ Oc 0z, (7)
where aits denotes the ground-truth -ouuh action at future step + k.
During training, we co-train the three tasks and switch between different tasks by changing the prompt (Appendix A.3). The final loss function is:
L
Σ TED
A. (0) + I (0) + In (0)
(5)
where the set of training trajectories.
For testing, only activate the prediction of actions, and the model predicts the next n actions and exccutes only the first one. This ensures fast, reactive navigation without reasoning overhead. We demonstrate its stabilizing effect in long-horizon trajectories through ablation studies (Table
4 Experimental Results
4.1 Experiment Setup
Simulated environments. We evaluate our method on the VLN-CE benchmarks R2R-CE 34 and RxR-CE 40) following the standard VI.N-CE settings. All the methods are evaluated on the R2R val-unseen split and RxR val-unseen split.
to evaluate the navigation performance for all the methods, including success rate (SR), oracle success rate (OSR), success weighted by path length (SPL), and navigation error from goal (NE).
Metrics. We follow the standard VLN evaluation protocol 34
40
4.2 Implementation Details
Model training. We use NVILA-lite 8B [7 as the base pretrained model, which consists of vision encoder (Si (58). projector, and an LLM (Qwen 2 [16).. We use supervised finetuning
7

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(SFT) to train our VLN model from stage 2 of NVILA-lite, as it has finished visual language corpus pre-training. Our model is trained with NVIDIA H20 GPUs for one epoch (around 60 hours), with learning rate of le-5.
The action space is designed into four categories: move forward, turn left, turn right, and stop. The forward action includes step sizes of 25 em, 50 cm, and 75 cm, while the turn actions are parameterized by rotation angles of 15 30 and 45 This iin-nnained design allows for more precise and flexible control, which is critical in complex environments.
Action design.
4.3 Comparison on VLN-CE Benchmarks
We evaluate our method on the VLN-CE benchmarks, which provide continuous environments for navigational actions in reconstructed photorealistic indoor scenes. We first focus on the val-unseen split in R2R-CE dataset in Table T To be fair, we distinguish between methods by marking those based on waypoint predictors (*) and those that are not based on large models (f).
In large model-based methods, we additionally mark the amount of data used by the method in addition to the R2R-CE training split (Extra Data). We further scale by the RxR training split (600K), DAgger data (500K) and web data (500K), and our performance achieves the SOTA Success Rate (SR) to those using much larger amount of training data.
Table 1: Comparison of different methods on the R2R Val- split. Observations used include Monocular (Mono.) and Panoramic view (Pano.). * indicates methods based on the waypoint predictor [.) i indicates methods without using LLMss. indicates the models using the training data only from R2R-CE training split, and we compare with the results reported in their paper for fair evaluation. The training data structures of traditional non-LLM-basse methods are quite different, so we do not compare them with their extra data.
Observation
R2R Val-Unseen
Training
Method
Venue
Mono. Pano.
NE 4
OSR
SR
SPL 1
Extra Data
BRVBent
7
ICCV2023
4.57
67.0
59.0
50.0
ETPNav
59
TPAM12024
4.71
65.0
57.0
49.0
660
Neurips2024
V
4.69
65
58
50
Seq2Seg
ECCV2020
7.77
37.0
25.0
22.0
CMA 34
ECCV2020
7.37
40.0
32.0
30.0
LAW [6
EMNLP2021
6,83
44.0
35.0
31.0
CM2 62
CVPR2022
7.02
41.0
34.0
27.0
WS-MGMap 12
Neurips2022
6,28
47.0
38.0
34,0
sim2real 63
CoRL2024
5.95
55.8
44.9
30.4
NaVid [9]
RSS2024
6.33
30.8
24.7
23.6
Aux-Think (ours)
6.01
52.2
46.0
40.5
OK
Uni-NaVid
RSS2025
5.58
53.3
47.0
42.7
5570K
NaVILA(1I
RSS2025
5.22
62.5
54.0
49.0
2770K
Aux-Think (ours)
6.08
60.0
54.8
46.9
1600K
As in [T] our proposed Aux-Think achieves strong performance with and without extra data. We attribute these results to multilevel reasoning supervision. Our joint training on CoT-based reasoning, instruction reconstruction, and receding-horizet grounding and decision-making making ability, allowing it to better generalize from limited data. Our method benefits from bessoning-inducee supervision signals that align more closely with the high-level semantic structure of the instructions, making each training example more informative.
action prediction enriches the model's semantic
To further assess the ability of Aux-Think, we evaluate it on the RxR-CE [40] Val-Unseen split (Table 2 Compared to R2R-CE, RxR-CE includes more natural instructions and longer trajectories, making it more realistic and challenging benchmark. Aux-Think achieves strong overall perfor- mance, particularly on the Success Rate (SR) metric, where it surpasses Uni-Na Vid and NaVILA while using much fewer training data (1920K vs. 5900K and 3100K). This demonstrates the ef- fectiveness of reasoning supervision under limited data. It is noted that performance on NE and SPI is relatively modest. This likely due the model's 's internalized reasoning behavior, learned during CoT training, which encourages broader exploration. Although this can lead to longer paths

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and reduced efficiency, it improves SR and OSR by increasing the likelihood of reaching the goal, especially in unfamiliar environments.
Table 2: Comparison of different methods on the RxR Val-Unseen split. indicates methods without using LLMs. The training data structures of traditional non- non-LLM-bass LLM-based methods are quite different, we do not include their training data in this table.
Observation
RxR Val Val-Unseen
Training
Method
Venue
Mono.
Pano.
NE 1
OSR
SR t
SPL f
Data
ETPNav 59
TPAM12024
5.64
54.7
44.8
-TP--a 60
Neurips2024
5.51
55.27
45.11
Seq2Seq
34
ECCV2020
11.8
13.9
11.9
LAWi
EMNLP2021
10.87
21.0
8.0
CM2
62
CVPR2022
12.29
25.3
14.4
9.2
sim2real
63
CoRI.2024
8.79
36.7
25.5
18.1
Uni-NaVid
RSS2025
6.24
55.5
48.7
40,9
5900K
NaVILA21
RSS2025
6.77
49.3
44.0
3100K
Aux-Think (ours)
6.24
61.9
52.2
40.2
1920K
4.4 Comparison Between Different Reasoning Strategies
As shown in Table 3 we compare different reasoning strategies on R2R-CE, with only the R2R- CoT-320K dataset as training data for fairness. We find that the SR performance of Pre- ThinThink and Post-Think is significantly lower than No-Think. In Pre-Think, the action prediction is conditioned on the generated CoT; thus, low-quality or poorly learned CoT directly impairs action accuracy. While Post-Think partially mitigates this issue by generating CoT after the action, suboptimal CoT representations still degrade overall performance. In contrast, the proposed Aux- Think decouples CoT and action learning by implicitly internalizing CoT knowledge into feature representations.
Table 3: Comparison of different reasoning strategies R2R-CE Val-Unseen split.
Reason Strategies
NE.
OSR1
SR1
SPL
Avg. time.
-hiThink
7.78
43.7
35.1
30.2
1.25s
Pre-Think
9.23
19.3
11.4
30.622
Post-Think
8.59
35.1
29.0
23.8
28.97s
Aux-Think (ours)
7.09
47.6
41.3
35.8
1.25s
In Fig. 6 we evaluate the Success Rate (SR) per test step for Aux-Think (ours), Pre- ThinThink, and Post-Think, with results grouped by the number of steps required for task completion. Across all step ranges, Aux-Thine consistently outperforms both baselines. A key observation that the performance of Pre-Think and Post-Think degrades sharply the required steps increase, with SR approaching zero for tasks exceeding 70 steps. In contrast, Aux-Think maintains strong performance even on longer-horizon tasks, exhibiting markedly higher robustness and generalization to complex, multi-step navigation scenarios. These results highlight the superior scalability of handling extended reasoning and decision-making under increased task complexity.
in
4.5
Ablation Studies
4.5.1
Impact of Different Auxiliary Tasks and Heceding-Horizon Action Planning
Table presents the ablation of three components: CoT-based Reasoning (A), Instruction based Reasoning (B), and Receding- Horizon Action Planning (C). Introducing CoT Reasoning (A) leads to a noticeable improvement across all metrics, indicating its effectiveness in guiding action decisions. Adding Non-CoT Reasoning (A+B) further enhances performance, suggesting that the two forms of reasoning are complementary. The full model (A+B+C), which incorporates receding-horian planning, achieves the best results, particularly terms of SPL and SR, demonstrating that long-term planning grounded in implicit reasoning yiclds the most robust behavior. These results validate the necessity of integrating both reasoning and planning for optimal performance.

---

0.6
ALaThERE Tounk
0.5
Success Rate (5R)
0.4
Rag (POSTINNG)
Ang Hhamk
thee Thinks
Number
500
-20
0.2
200
0.1
(0.20) (20,30)
400,400 (40.50) 70) lotal Action Steps
20
30
40
30
60
Total Action Steps
(b)
Figure 6: (a) Success Rate of reasoning strategies on different steps during testing. (b) The epsodes number different steps during testing. The steps indicate the actions required by the instruction.
Table 4: Ablation study different components. A: CoT Reasoning, B: Non-CoT Reasoning, C: Receding- -oriinon Action Planning.
Table 5: Ablation studies the predicted steps in our Receding-Horipn Action Planning. The best one across all metrics when the number of steps is 3.
Configuration
Metrics
A
B
C
NEJ
OSR
SR1
SPLt
#Steps
OSRI
SR
SPL.1
7.78
43.7
35.1
30.2
1
7.78
43.7
35.1
30.2
7.08
47.6
41.3
35.8
2
7.88
44.2
35.8
30.9
7.12
46.3
40.6
35.7
3
7.14
47.3
41.4
36.1
V
7.14
47.3
37.1
32.2
l
6.92
49.1
44.2
38.9
4
7.50
43.6
36.4
31.8
d
52.2
46.0
40,5
5
7.54
43.7
37.1
32.2
4.5.2 Impact of Steps in Receding-Horizon Action Planning
Based on Table 5 the model achieves the best performance when the number of predicted steps is set to 3. We disable the CoT auxiliary task to analyze the impact of actions more clearly. The results highlight our Receding-Horin Horizon Action Planning, which encourages the model to anticipate future actions and enhances its planning capabilities. However, increasing the number of predicted steps beyond this point leads to performance degradation. We attribute this to the limited perceptual field of monocular observations without additional global knowledge, which makes long-horizon prediction more challenging and can cause the model to generate suboptimal or collapsed navigation behaviors.
5 Limitation and Future Work
This work evaluates Aux-Think' ' data efficiency under a controlled, widely adopted setup: SFT on the R2R dataset with monocular RGB input. This enables fair comparison and isolates the effect of reasoning- aware supervision. While constrained, this setting opens future directions, scaling to larger navigation datasets and incorporating richer supervision (c.g., depth, panorama, localization)
42
6 Conclusion
We conduct the first systematic investigation of reasoning strategies in Iision-and-Lanuuaae Naviga- tion, revealing a key limitation, Test-time Reasoning Collapse, where errors in generated reasoning can compound and degrade navigation performance. Motivated by this finding, we propose Aux-Think, reasoning-award co- to-training framework that leverages Chain-of- Thiough--Thhhttttt as auxiliary supervision during training, while relying on efficient No-Think testing. Extensive experiments demonstrate that Aux-Think achieves performance on par with state-of-the-arr methods while using significantly less training data, highlighting its robustness and data efficiency. also release R2R-CoT-320k, the first CoT dataset for VLN, to facilitate future research reasoning models.
10

---

7 Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant No. 62441617. It was supported by the Postdoctoral Fellowship Program and China Postdoctoral Science Foundation under Grant No. 2024M764093 and Grant No. BX20250485, the Beijing Natural Science Foundation under Grant No. 4254100, the Fundamental Research Funds for the Central Universities under Grant No. KG16336301, and by Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing. Deying Li was supported in part by the National Natural Science Foundation of China Grant Nlo.2207447 Yongcai Wang was supported in part by the National Natural Science Foundation of China Grant No. 61972404, Public Computing Cloud, Remmin University of China, and the Blockchain Lab, School of Information, Renmin University of China. Shuo Wang was supported in part by the Outstanding Innovative Talents Cultivation Funded Programs 2024 of Renmin University of China.
References
[1] Stéphane Ross, Geoffrey Gordon, and Drew Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the fourteenth interna- tional conference on artificial intelligence and statistics, pages 627-635. JMLR Workshop and Conference Proceedings, 2011.
[2] Wansen Wu, Tao Chang, Xinmeng Li, Quanjun Yin, and Yue Hu. Vision-language navigation: survey and taxonomy. Neural Computing and Applications, 36(7):3291-3316 2024.
[3] Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould, and Anton Van Den Hengel. Vision-and lannn-a navigation: Interpreting visually grounded navigation instructions in real environments. In Proceedings of the IEEE conference on computer vision and patlern recognition, pages 3674-3683, 2018
[4] Jing Gu, Eliana Stefani, Qi Wu, Jesse Thomason, and Xin Eric Wang. Vision-and-language navigation: A survey of tasks, methods, and future directions. arXiv preprint arXi::2203.12667 2022.
[5] Yicong Hong, Zun Wang, Qi Wu, and Stephen Gould. Bridging the gap between learning in discrete and continuous environments for visoon-and-languag navigation. In Proceedings of the IEEE/CVF conference computer vision and paltern recognition, pages 15439-15449, 2022.
[6] Zihan Wang, Xiangyang Li, Jiahao Yang, Yeqi Liu, and Shuqiang Jiang. Gridmm: Grid memory map for vision-and and-language navigation. In Proceedings of the IEEE/CVF International conference on computer vision, pages 15625-15636, 2023.
[7] Dong An, Yuankai Qi, Yangguang Li, Yan Huang, Liang Wang, Tieniu Tan, and Jing Shao. Bevbert: Multimodal map pre-training for language-gun guided navigation. arXiv preprint arXiv:2212.04385 2022.
[8] Jacob Krantz, Aaron Gokaslan, Dhruv Batra, Stefan Lee, and Oleksandr Maksymets. Waypoint models for intructiio-guided navigation in continuous environments. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pages 15162-15171, 2021.
[9] Jacob Krantz and Stefan Lee. Sim-2-sim transfer for lannauag navigation in con- tinuous environments. In European conference computer vision, pages 588 088603..6 Springer, 2022.
[10] Muhammad Zubair Irshad, Niluthpol Chowdhury Mithun, Zachary Seymour, Han-Pang Chiu, Supun Samarasekera, and Rakesh Kumar. Sasra: Semantically spatio-temporal reasoning agent for vision-and aawioon-nn--aanuuu language navigation in continuous environments, arXiv e-prints, pages arXiv-2108, 2021.
[11] Kevin Chen, Junshen K Chen, Jo Chuang, Marynel Vázquez, and Silvio Savarese. Topological planning with transformers for sidio-and--lnguaag navigation. In Proceedings of the IEFF/VV Conference Computer Vision and Pattern Recognition, pages 11276-1 1286, 2021.
11

---

[12] Peihao Chen, Dongyu Ji. Kunyang Lin, Runhao Zeng. Thomas Li, Mingkui Tan, and Chuang Gan. Weally-supervised multi- gualil-gannlrrity map learning for vision-an aosoon-nndllannn -anguaga navigation. Advances in Neural Information Processing Systems, 35:38149 49-38161,, 2022.
[13] Shuo Wang, Wanting Li, Yongcai Wang, Zhaoxin Fan, Zhe Huang, Xudong Cai, Jian Zhao, and Deying Li. Mambavo: Deep visual odometry based on sequential matching refinement and training smoothing. In Proceedings of the Computer Vision and Pattern Recognition Conference,
pages 1252-1262 2025.
[14] An Yang. Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2. 5 technical report. arXiv preprint arXiv:2412. 15115, 2024.
[15] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Amin Lachaux, Timo- thée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro. Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302. 2023.
[16] Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang. Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923, 2025.
[17] Zhijian Liu, Ligeng Zhu, Baifeng Shi, Zhuoyang Zhang. Yuming Lou, Shang Yang. Haocheng Xi, Shiyi Cao, Yuxian Gu, Dacheng Li, et al. Nvila: Efficient frontier visual language models. arXiv preprint 2rXiv:24/2.04445 2024.
[18] Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. arXiv preprint arXiv:2311.10122, 2023.
[19] Jiazhao Zhang, Kunyu Wang, Rongtao Xu, Gengze Zhou, Yicong Hong, Xiaomeng Fang, Qi Wu, Zhizheng Zhang, and He Wang. Navid: Video-based vim plans the next step for vision-and-language navigation. arXiv preprint 220.:::202.1155522 2024.
[20] Jiazhao Zhang, Kunyu Wang, Shaoan Wang, Minghan Li, Haoran Liu, Songlin Wei, Zhongyuan Wang, Zhizheng Zhang, and He Wang. Uni-navid: A video-based vision-language-accioo model for unifying embodied navigation tasks. arXiv preprint arXiv:2412.06224 2024.
[21] An-Chieh Cheng, Yandong Ji, Zhaojing Yang. Zaitian Gongye, Xueyan Zou, Jan Kautz, Erdem Bıyik, Hongxu Yin, Sifei Liu, and Xiaolong Wang. Navila: Legged robot vision- ssiionlanguago-actioo model for navigation. arXiv preprint 24Xi:22122.04453. 2024.
[22] Fengda Zhu, Yi Zhu, Xiaojun Chang, and Xiaodan Liang. Vision-language navigation with self-supervised auxiliary reasoming tasks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10012-10022, 2020.
[23] Kuo-Hao Zeng, Zichen Zhang, Kiana Ehsani, Rose Hendrix, Jordi Salvador, Alvaro Herrasti, Ross Girshick, Aniruddha Kembhavi, and Luca Weihs. Poliformer: Scaling on on-policy rl with transformers results in masterful navigators. arXiv preprint arXiv:2406.20083, 2024.
[24] Mingfei Han, Liang Ma, Kamila Zhumakhanova, Ekaterina Radionova, Jingyi Zhang, Xiaojun Chang, Xiaodan Liang, and Ivan Laptev. Roomtour3d: Reometry-awar video-instruction tuning for embodied navigation. arXiv preprint arXiv:2412.08577 2024.
[25] Duo Zheng, Shijia Huang, Lin Zhao, Yiwu Zhong, and Liwei Wang. Towards learning generalist model for embodied navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13624-13634 2024.
[26] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoe V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. 2022.
Advances in neural information processing systems,
[27] Kaituo Feng Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang. Tianshuo Peng, Benyou Wang, and Xiangyu Yue. Video-rl: Reinforcing video reasoning milms. arXiv preprint 203 2025.
12

---

[28] Zhengxi Lu, Yuxiang Chai, Yaxuan Guo, Xi Yin, Liang Liu, Hao Wang, Guanjing Xiong, and Hongsheng Li. Ui-rl: Enhancing action prediction of gui agents by reinforcement learning. arXiv preprini MxNiv:2503.2162200 2025.
[29] Xuan Zhang, Chao Du, Pang, Qian Liu, Wei Gao, and Min Lin. Chain of preference optimization: Improving chain-of-thought reasoning in llms. Advances in Neural Information Processing Systems, 37:333-356, 2024.
Pang,
[30] Ming Li, Jike Zhong. Shitian Zhao, Yuxiang Lai, and Kaipeng Zhang. Think or not think: study of explicit thinking in rule-based visual reinforcement fine-tuning. arXiv e-prints, pages arXiv-2503, 2025.
[31] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015.
[32] Yan Wang, Yawen Zeng, Jingsheng Zheng, Xiaofen Xing, Jin Xu, and Xiangmin Xu. Videocot: video chain-of-thought dataset with active annotation tool. arXiv preprint arXiv:2407.05355 2024.
[33] Jonathan St BT Evans. In two minds: dual-process accounts of reasoning. Trends in cognitive sciences, 7(10):454-459 2003.
[34] Jacob Krantz, Erik Wijmans, Arjun Majumdar, Dhruv Batra, and Stefan Lee. Beyond the nav- graph: Aision-anddlanguag navigation in continuous environments. In Computer ECCinnEECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXVIII 16, pages 104-120. Springer, 2020.
[35] Khanh Nguyen, Debadeepta Dey, Chris Brockett, and Bill Dolan. Vision-based navigation with language-based assistance via imitation learning with indirect intervention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12527-12537, 2019.
[36] Hanqing Wang, Wei Liang, Luc V Gool, and Wenguan Wang. Towards versatile embodied navigation. Advances in neural information processing systems, 35:36858-3633 2022.
[37] Qiaoyun Wu, Xiaoxi Gong, Kai Xu, Dinesh Manocha, Jingxuan Dong, and Jun Wang. Towards target-driven visual navigation in indoor scenes via generative imitation learning. IEEE Robotics and Automation Letters, 6(1):175-182 2020.
[38] Zifan Xu, Bo Liu, Xuesu Xiao, Anirudh Nair, and Peter Stone. Benchmarking reinforcement learning techniques for autonomous navigation. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages
IEEE, 2023.
[39] Xin Wang, Qiuyuan Huang, Asli Celikyilmaz, Jianfeng Gao, Dinghan Shen, Yuan-Fang Wang, William Yang Wang, and Lei Zhang. Visionnlanguage navigation policy learning and adaptation. IEEE transactions on pattern analysis and machine intelligence, 43(12):4205 420044225--216,,,,,- 2020.
[40] Alexander Ku, Peter Anderson, Roma Patel, Eugene Ie, and Jason Baldridge. Room-across- room: Multilingual lsiionand-llnnguage navigation with dense spatiotemporal grounding. arXiv preprint arXiv:2010.00954 2020.
[41] Gengze Zhou, Yicong Hong, and Qi Wu. Navgpt: Explicit reasoning vision-and-languae navigation with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 7641-7644 2024.
[42] Shuo Wang, Yongcai Wang, Wanting Li, Yucheng Wang, Maiyue Chen, Kaihui Wang, Zhizhong Su, Xudong Cai, Yeying Jin, Deying Li, et al. Monodream: Monocular vision-language navigation with panoramic dreaming. arXiv preprint arXiv:2508.02549 2025.
[43] Yuxing Long, Xiaoqi Li, Wenzhe Cai, and Hao Dong. Discuss before moving: Visual language navigation via multi-exper discussions. In 2024 IEFE International Conference on Robotics and Automation (ICRA), pages 17380-17387 IEEE, 2024.
13

---

[44] Yuxing Long, Wenzhe Cai, Hongcheng Wang. Guanqi Zhan, and Hao Dong, Instructnav: Zero-shot system for generic instruction navigation in unexplored environment. arXiv preprint 0rXiv:2406.04888, 2024.
[45] Dhruv Shah, Blažej Osiński, Sergey Levine, et al. Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action. In Conference on robot learning, pages 492-504. PMIR, 2023.
[46] Devendra Singh Chaplot, Dhiraj Prakashchand Gandhi, Abhinav Gupta, and Russ R Salakhut- dinov. Object goal navigation using goal-oriented semantic exploration. Advances in Neural Information Processing Systems, 33:4247-4258, 2020.
[47] Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra. Embodied question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1-10, 2018.
[48] Md Jahidul Islam, Jungseok Hong, and Junaed Sattar. Person-following by autonomous robots: categorical overview. The International Journal of Robotics Research, 38(14)11511-1618
2019.
[49] Xavier Puig, Eric Undersander, Andrew Szot, Mikael Dallaire Cote, Tsung- Yen Yang. Ruslan Partsey, Ruta Desai, Alexander William Clegg, Michal Hlavac, So Yeon Min, et al. Habitat 3.0: co-habitat for humans, avatars and robots. arXiv preprint arXiv:2310.13724, 2023.
[50] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR), 2023.
[51] Timo Schick, Jane Dwivedi-Ye Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to tools. Advances in Neural Information Processing Systems, 36:68539 68551, 2023.
[52] Ziyu Liu, Zeyi Sun, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Haodong Duan, Dahua Lin, and Jiaqi Wang. Visual-rft: Visual reinforcement fine-tuning. arXiv preprint arXiv:2503.0/785, 2025.
[53] Zhenyi Liao, Qingsong Xie, Yanhao Zhang. Zijian Kong, Haonan Lu, Zhenyu Yang, and Zhijie Deng. Improved visual-spatial reasoning via -l-zero-like training. arXiv preprint arXiv:2504.00883: 2025.
[54] Junjie Wen, Minjie Zhu, Yichen Zhu, Zhibin Tang, Jinming Li, Zhongyi Zhou, Chengmeng Li, Xiaoyu Liu, Yaxin Peng, Chaomin Shen, et al. Diffusion-vla: Scaling robot foundation models via unified diffusion and autoregression. arXiv preprint arXhv:2412.03293 2024.
[55] Michal Zawalski, William Chen, Karl Pertsch, Oier Mees, Chelsea Finn, and Sergey Levine Robotic control via embodied chain-of-thought reasoning. arXiv preprint arXiv:2407.08693, 2024.
[56] Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra. Habitat: A Platform for Embodied AI Research. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019.
[57] Daya Guo, Dejian Yang. Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-rl: Incentivizing reasoning capability in Ilms via reinforcement learning. arXiv preprini arXiv:2501. 2025.
[58] Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alab- dulmohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, et al. Siglip Multilingual vision-language encoders with improved semantic understanding. localization, and dense features. arXiv preprint arXiv: 200. 2025.
14

---

[59] Dong An, Hanqing Wang, Wenguan Wang, Zun Wang, Yan Huang, Keji He, and Liang Wang. Etpnav: Evolving topologiaa planning for vision-language navigation in continuous environments. IEEE Transactions Pattern Analysis and Machine Intelligence, 2024.
[60] Rui Liu, Wenguan Wang, and Yi Yang. Vision-language navigation with energy-based policy, arXiv preprint 24Xhv22410.122500 2024.
[61]
Sonia Raychaudhuri, Saim Wani, Shivansh Patel, Unnat Jain, and Angel Chang. Language- aligned waypoint (LAW) supervision for ssiionanddlanggaag navigation in continuous vironments. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 401 4018-4028, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics.
[62] Georgios Georgakis, Karl Schmeckpeper, Karan Wanchoo, Soham Dan, Eleni Miltsakaki, Dan Roth, and Kostas Danilidis. Cross-modal map learning for vision and language navigation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1 15460-15470, 2022.
[63] Zihan Wang, Xiangyang Li, Jiahao Yang, Yeqi Liu, and Shuqiang Jiang. Sim-to-real transfer via 3d feature fields for vision-and and-language navigation. arXiv preprint arXiv:2406.09798, 2024.
[64] Peihao Chen, Xinyu Sun, Hongyan Zhi, Runhao Zeng, Thomas Li, Gaowen Liu, Mingkui Tan, and Chuang Gan. A2 nav: Action-aware zero-shot robot navigation by exploiting vision- and-language ability of foundation models. arXiv preprint 230i:::300..79999 2023.
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16. Declaration of LLM usage
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Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, or originality the research, declaration is not required.
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A Technical Appendices and Supplementary Material
A.1 More Experimental Results
We evaluate the cross-data performance on RxR-CE Val-Unseen split, as shown in Table 6 Even without using RxR-CE training data, our Aux-Think model achieves new state-of-the-ar performance on the RxR-CE Val-Unseed split. This confirms the strong generalization of our reasoning caasoing-auugmented augmented co-training, enabling the model to transfer across datasets with different instructions and scenes.
Table 6: Cross-dataset performance on the RxR-CE Val-Unseen split. All results are obtained without training on the RxR-CE training set.
Observation
RxR Val-Unseen
Method
Venue
Mono. Pano.
4
OSR f
SR f
SPL f
Seq2Seq 34
ECCV2020
11.8
5.02
3.51
3.43
CMA
ECCV2020
11.7
10.7
4.41
2.47
LAW[61
EMNLP2021
10.87
21.0
8.0
8.0
CM2 62
CVPR2022
8.98
25.3
14.4
9.2
WS-MGMap 12
Neurips2022
9.83
29.8
15.0
12.1
NAA 66
Arxiv2023
16.8
6.3
NaVid
RSS2024
8.41
34.5
23.8
21.2
Aux-T mux-Think (ours)
8.98
39.6
29.5
23.6
A.2 R2R-CoT-320k
The action labels in R2R-CoT-320K are derived from the original annotations in R2R-CE. To generate the Chain-of-Thought (CoT) annotations, we employ Qwen-VIL 2.5 (72B). Specifically, for each navigation step, we provide the model with the agent's historical observations, the current visual input, and the next action. The model then prompted to produce intermediate reasoning steps that reflect human-like like decision- maainng processes. The annoation prompt is:
Imagine you robot programmed for navigation tasks. You have been given video of historical observations: Simage>...<imagee and and current observation: <image>. Your assigned task is: [Instruction]. Analyze this series of images decide your next move, which could involve turning left or right by specific degree, moving forward a certain distance, or stop if the task is completed. The final answer is [Action]. Please think about this question as if you were a human pondering deeply. Engage in an internal dialogue using expressions such as 'Iet me think', 'wait', 'Hmm', 'oh, I see", 'let's break down', etc, or other natural language thought expressions. It's encouraged to include self-reflection or verification in the reasoning process.
To provide deeper quantitative understanding of proposed R2R-CoT-3202 dataset, present statistics on CoT content and complexity. As shown in Fig. 7a the word cloud reveals frequent reasoning patterns grounded in spatial semantics, such "doorway," "current observation." "hallway," "turning," and "goal." These tokens suggest that the dataset captures rich, step-by-step reasoning tightly aligned with embodied navigation semantics.
Fig. 7b shows the distribution of CoT lengths, where most reasoning chains fall within the 200-300 word range, but with long tail reaching beyond 450 words. This indicates that the dataset covers both concise and highly detailed reasoning processes, posing a greater challenge than typical short-form CoT datasets used in static tasks.
augmeetee dataset for VIN with diverse, high-coverage CoT annotations. It offers a valuable benchmark to study the role of language- based reasoning in long-horizon, partially observable navigation tasks.
Overall, R2R-CoT-320k represents the first large-scale
A.3 Navigation Prompts
We use the following prompt to drive the model to predict navigation actions:
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doorway ins instruction
Sutriration GoT langte
current
M sequeiia e
d
ROOM
On
a aligg tattrrooo
rome
turning goal
lnage shok,,
breek
pathoire
bedr oom
Dedroom
diven attatartte
kitchen
paa wayy
stop
door
Fark Wai idering
movement
stea
(a)
(b)
Figure 7: (a) Word cloud of Chain-of- Chain-of-Thoughn in the R2R-CoT-3201 dataset, highlighting frequent visual and spatial reasoning patterns. (b) Distribution of CoT lengths (in word count), showing wide and diverse range of reasoning complexity.
Imagine you a robot programmed for navigation tasks. You have been given video of historical observations: <image>....<image and and current observation: <image>, Your assigned task is: [Instruction]. Analyze this series of images decide your next move, which could involve turning left or right by specific degree, moving forward a certain distance, or stop if the task is completed.
Among them, [Instruction] is the language instruction given for the current task. For the auxiliary task of CoT-based reasoning, we add "Please provide your step-by-step reasoning process" after the above prompt.
For the Non-CoT Instruction Reasoning, set the prompt as:
Assume you robot designed for navigation. You provided with captured image quences: trajectory of the robot.
Based on this image sequence, please describe the navigation
A.4 More Details About Pre-Think and Post-Think
To further investigate Test-time Reasoning Collapse (TRC) phenomenon, introduce special tokens to delineate the reasoning and action prediction components. During training, we assign different loss weights the reasoning component, shown in Table We observe that moderately reducing the CoT Chhin-of-Thhughhe loss weight improves the performance of both Pre-Think and Post-Think. However, their performance still lags behind that of No-Think and Aux-Think.
Our results reveal a consistent trend: reducing the CoT loss weight moderately improves performance For example, in the Post- Think setting, decreasing the CoT
for both Tre-Think and weight from to 0.1 leads to a +1.8% SR improvement (from 29.0 to 30.8). This suggests that over-reliance on CoT can introduce noise, potentially due its misalignment with the suboptimal, off-distribution states encountered during testing.
However, even with carefully tuned weights, both strategies still fall short of No-Think. For instance, No-Think achieves 35. 15. SR, significantly outperforming the best Post-Think variant (30.8%) and Pre-Think (15.9%). This persistent gap underscores deeper issue: while CoT can serve as useful supervision during training, explicitly generating and relying on CoT during testing is inherently brittle in VLN due to compounding errors and distribution shift. This reinforces our finding that VLN agents suffer from reasoning collapse when required to generate structured thoughts in real time within dynamic, partially observable environments.
In summary, despite our extensive efforts to optimize Pre-Think and Post-Think through CoT loss reweighting and architectural adjustments, these strategies fail to match the robustness and effective- ness of CoT-free testing (No- -hink.. These findings motivate our proposal of Aux-Think, which leverages the strengths of CoT through auxiliary supervision while circumventing the vulnerabilities of test-time reasoning.
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Table 7: Experiments on the impact of CoT Loss weight on Pre- Think and strategies.
Weight of CoT Loss
NE 4
OSRT
SR
SPL1
1
9.23
19.3
11.4
8.6
Pre-Think
0.1
9.42
28.3
15.9
12.8
0.01
8.84
20.6
12.2
9.3
1
8.59
35.1
29.0
23.8
Post-Think
0.1
8.50
37.7
30.8
24.7
0.01
8.25
36.6
29.3
24.9
No-Think
7.78
43.7
35.1
30.2
25


Reviews:
[{"review_id": "gYP1PRAbAu", "reviewer": "Reviewer_gjCb", "summary": "This paper explores integrating reasoning strategies into Vision-and-Language Navigation (VLN). A surprising finding is that inference-time reasoning degrades performance, which is named by the authors as “Inference-time Reasoning Collapse (IRC)”. Based on this insight, they propose Aux-Think, a framework that uses Chain-of-Thought (CoT) reasoning only during training as auxiliary supervision, while performing inference without explicit reasoning. Experimental results show that Aux-Think outperforms other reasoning strategies and demonstrates promising data efficiency.", "questions": "Please see my comments on weaknesses above.", "limitations": "The paper does have limitation section.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\nThe paper provides the first systematic analysis of reasoning strategies for VLN, highlighting a counterintuitive but important finding: inference-time CoT reasoning hurts performance in long-horizon, partially observable environments. This contributes a novel insight to the field.\n\nWeaknesses:\nThe experimental results are not very strong. At present, the performance is not on par with the state-of-the-art baseline NaVILA. It is understandable that CoT data is more difficult to collect, and thus the authors could not scale up training to the same extent. However, this inevitably weakens the paper—both from the perspective of scalability and from that of data efficiency.\n\nThe scalability of the method remains unproven, as no large-scale experiment has been conducted to establish whether the approach can match or exceed existing methods when trained on comparable amounts of data.\n\nEvidence for data efficiency is also limited. A more convincing demonstration would involve directly comparing the proposed method against strong baselines—e.g., fine-tuning a pretrained NaVILA model—under identical small-data regimes and in the same unseen environments. Importantly, the comparison should be with a pretrained NaVILA rather than one trained from scratch. Since collecting CoT data is a clear limitation of the proposed approach, it is critical to show that even with a limited amount of CoT supervision, the method can outperform models trained on large-scale non-CoT data, which is significantly easier to obtain.", "was_revised": true, "final_justification": "My primary concern is stated in the weakness part, regarding the scalibility of the method since It does not outperform SOTA model. The new experiment results show that increasing data size does improve the performance and it is now on par with or slightly better than the SOTA. Although it is not clear about the further scalibility, I believe this is enough for me to raise my score.", "rebuttal": "**Q1: The experimental results and the scalability of our method**\n\n**A1:** Thanks for the good question.  We provide the additional scalability experiment by increasing the total training size from 0.9M to 1.9M samples to address your concern:\n\nTable R1: Updated experimental results on R2R-CE benchmark\n| R2R-CE |  Version|Data Size  | NE ↓   |  OSR ↑   | SR ↑   | SPL ↑ | \n| ------------| -------------- | -------------- | -------- | ------------ | -------- | ------- |\n| NaVILA  | -|3.1 Million | **5.22**    | 62.5   | 54.0   | **49.0** |\n| Aux-Think | Initial Submission|0.9 Million  | 5.88 |54.9         |49.7    | 41.7   |\n| Aux-Think | Update in Rebuttal|1.9 Million  |5.49  | **62.9**  |**55.7** |48.7  |  \n \n\nTable R2:  Updated experimental results on RxR-CE benchmark\n| RxR-CE  |  Version |  Data Size | NE ↓   |  OSR ↑   | SR ↑   | SPL ↑ | \n| ------------| -------------- | -------------- | -------- | ------------ | -------- | ------- |\n| NaVILA  | -|3.1 Million | 6.77    | -              | 49.3   | **44.0**   |\n| Aux-Think | Initial Submission|0.9 Million  | 6.91 |60.1         |49.3    | 37.9    |\n| Aux-Think | Update in Rebuttal |1.9 Million  | **6.40**  | **63.9**  |**52.4** |39.9    |\n\n- We conduct a new large-scale experiment by increasing the total training size from 0.9M to 1.9M samples. The extended training set includes 500K DAgger samples and 500K YouTube navigation samples annotated using the same pipeline as NaVILA. \n- Due to the incomplete release of NaVILA’s data, we are only able to utilize approximately 60% of its total training data. Even so, with this  training set, our model achieves comparable performance to NaVILA on both the R2R-CE and RxR-CE benchmarks. These results demonstrate that our method continues to improve as more data becomes available, confirming its scalability.\n\n\n**Q2: Evidence for data efficiency and the possibility to finetune NaVILA**\n\n\n**A2:** Thank you for the insightful comments. \n\n- We agree that comparing with a pretrained NaVILA under matched small-data settings would further strengthen the evidence for data efficiency. While NaVILA’s full training data and finetuning code were not publicly available at the time of submission (and remain partly unavailable now), we made our best effort to approximate the reviewer's intent using the available data. We designed a new experiment guided by the same motivation: to test whether our method performs comparably with less or reduced data.\n\n- Specifically, we collected the publicly released non-CoT portion of NaVILA’s training data (~ 50%, 1.58 Million) and combined it with the proposed CoT data (~ 10%, 0.32M), serving as a counterpart to the remaining ~ 50%. This gives a training set roughly 60% (1.9M) the size of NaVILA’s original (3.1M). Under this setup, Aux-Think achieves comparable performance to NaVILA on both R2R-CE and RxR-CE (see the above Table R1 and R2). This provides concrete evidence for the data efficiency of our method."}, {"review_id": "W3RBgnisxl", "reviewer": "Reviewer_juUc", "summary": "This paper introduces an interesting Aux-Think approach that incorporates two reasoning-based auxiliary tasks during training. Specifically, the authors propose:\n\n1. A CoT-based reasoning generation task, in which the model must predict chain-of-thought traces from {instruction, observation history}.\n2. An inverse instruction reasoning task, in which the model must recover the instruction from visual observations.\n\nThey retain a primary task of predicting a sequence of actions based on the instruction, observation, and navigation history. As shown in Table 3, this approach yields improvements in success rates on visual navigation tasks, whereas both pre-think and post-think neither improve upon the baseline nor avoid degrading performance. The proposed method is simple and easy to follow; however, the authors should explore whether injecting noise into states and actions during training could more principledly address the performance drop observed with both pre-think and post-think.", "questions": "The primary concern is how the authors train the pre-/post-thinking schemes and whether using more advanced PPO variants (e.g., GRPO, DAPO, VAPO) would help.\n\nPlease also address the other concerns raised above under **Weaknesses**:\n\n1. **Receding-horizon planning gains:** According to Table 4, most improvements arise from the receding-horizon action planning component. Predicting the next *n* actions from the instruction and prior observations may be trivial and adds limited insight. Furthermore, the method is highly sensitive to the chosen number of planning steps.\n2. **Unclear CoT vs. pre-think distinction:** A major open question is: What are the fundamental differences between the CoT-based reasoning task and the pre-reasoning (pre-think) scheme?\n3. **Dataset construction details:** The authors omit crucial information about how the R2R-CoT-320k dataset was constructed in the main paper, instead relegating it to the supplementary material.", "limitations": "No.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "* **Strengths:**\n\n  * The observation that pre-think and post-think reasoning can hurt performance is compelling, and the analysis offers a plausible explanation.\n  * However, this analysis is rather general and likely applies to many settings where pre-/post-think fails. The key challenge is bridging the gap between the high-quality, oracle reasoning available during training and the noisier reasoning that occurs at inference time.\n\n* **Weaknesses:**\n\n  1. **Receding-horizon planning gains:** According to Table 4, most improvements arise from the receding-horizon action planning component. Predicting the next *n* actions from the instruction and prior observations may be trivial and adds limited insight. Furthermore, the method is highly sensitive to the chosen number of planning steps.\n  2. **Unclear CoT vs. pre-think distinction:** A major open question is: What are the fundamental differences between the CoT-based reasoning task and the pre-reasoning (pre-think) scheme?\n  3. **Dataset construction details:** The authors omit crucial details about how the R2R-CoT-320k dataset was constructed in the main paper, instead hiding them in the supplementary material.\n  4. **Lack of alternative RL baselines:** The core approach hinges on combining two reasoning-based auxiliary tasks, yet the authors do not report whether they tried recent PPO variants (e.g., GRPO, DAPO, VAPO) to train the model to generate reasoning traces before or after producing the final action sequence.", "was_revised": true, "final_justification": "The reviewer believes the core questions about the RL findings remain unaddressed, particularly the lack of experimental evidence:\n\n- Fundamental blockers for RL failure: No ablation studies or diagnostic experiments provided to identify these blockers.\nReward function design: Missing experimental comparisons with alternative reward formulations beyond simple format/action rewards.\n- VLM limitations: No experiments correlating VLM base performance with RL effectiveness, or results comparing RL on stronger vs. weaker base models.\n- DAPO/VAPO analysis: Lacks quantitative results, performance numbers, or learning curves comparing these variants.\n\nWithout experimental evidence and systematic ablations, readers cannot determine if your negative RL results are a significant finding or just implementation-specific limitations. The authors' strong claims about RL's ineffectiveness need empirical support. The reviewer is willing to reconsider my evaluation if these points are addressed with concrete experimental results.", "rebuttal": "**Q1: The ***PRIMARY*** concern is how the authors train the pre-/post-thinking schemes and whether using more advanced PPO variants (e.g., GRPO, DAPO, VAPO) would help.**\n\n**A1:** Thanks for your comments. We would like to clarify two key points regarding your concerns:\n\n**(1) Clarification on our scope of algorithmic contribution.**\n- There appears to be a misunderstanding about how our models are trained. We use supervised fine-tuning (SFT) (**Lines 130, 211, 278**), which is the same training paradigm used by other state-of-the-art VLA-based navigation methods. Optimizing reasoning policies with advanced RL variants is orthogonal to our goal. Our contribution is to systematically analyze reasoning strategies in VLN and introduce Aux-Think, a training framework that uses CoT only during training as auxiliary supervision.\n\n- As an exploratory alternative, we also implemented GRPO-based reinforcement learning to train different reasoning strategies (**Appendix A.4**). This was done to more comprehensively investigate the learning behavior of different reasoning strategies. \nHowever, we observed that this reinforcement learning approach underperforms compared to our final method (**Line 519**), the SFT-based Aux-Think framework.\n\n- We also note that the reviewer suggests replacing “basic PPO” with GRPO or other PPO variants. We clarify that our RL implementation already uses GRPO, not vanilla PPO, as described in **Lines 231 and 519**, with analysis in **Lines 522–544** in our paper. We hope our response clears up the confusion.\n\n\n**(2) Additional GRPO Experimental Details**\nAlthough reinforcement learning is not the focus of our paper, we include the relevant experimental details here for completeness. We implemented GRPO with two types of reward signals following [1]:\n\n- Format reward: Indicates whether the output follows the structured reasoning format (“The next action is [action]”).\n- Action reward: Indicates whether the predicted action matches the oracle navigation step.\n\nWe applied GRPO after the SFT as cold start, to No-Think, Pre-Think, Post-Think, and our Aux-Think strategies, and we report the results below:\n\n|Method | Training Strategy | NE↓ | OSR↑ | SR↑ | SPL↑ |\n| :--- | :---: |  :---: | :---: | :---: | :---: |\n| No-Think| SFT | 7.78 | 43.7 | 35.1 | 30.2 |\n| No-Think| GRPO | 7.89 | 45.7 | 34.7 | 30.1 |\n| Pre-Think |SFT| 9.23 | 19.3 | 11.4 | 8.6 |\n| Pre-Think |GRPO| 9.01 | 21.0 | 9.0 | 8.2 |\n| Post-Think|SFT| 8.59 | 35.1 | 29.0 | 23.8 |\n| Post-Think|GRPO| 8.89 | 34.8 | 28.4 | 23.5 |\n| Aux-Think |SFT| **7.09** | 47.6 | **41.3** | **35.8** |\n| Aux-Think |GRPO| 7.23 | **48.6** | 40.1 | 33.4 |\n\nWe found that applying GRPO generally led to no improvement or even slight degradation in SR and SPL, which are the most important metrics in VLN. In particular, while the OSR score sometimes improved after GRPO, this increase is not indicative of meaningful navigation success: we observed that GRPO-trained models tend to take random exploratory actions when they are unsure whether to stop, which incidentally leads them to pass through the goal location without correctly stopping, thus inflating OSR without increasing SR.\n\nGRPO did not bring consistent benefits but harmed performance, we chose not to highlight these results in the main paper, nor to claim them as part of our contributions. We instead focused our analysis on the supervised (SFT-only) setting, which more clearly illustrates the advantages of our Aux-Think strategy.\n\n\nWe hope these key points clarify the reviewer’s concerns and helps accurately position the contribution of our work. We will revise the paper to make these points clearer where necessary.\n\n#### Reference:\n\n[1] Feng, K., Gong, K., Li, B., Guo, Z., Wang, Y., Peng, T., ... & Yue, X. (2025). Video-r1: Reinforcing video reasoning in mllms. arXiv preprint arXiv:2503.21776.\n\n\n\n**Q2: Receding-horizon planning gains: According to Table 4, most improvements arise from the receding-horizon action planning component. Predicting the next n actions from the instruction and prior observations may be trivial and adds limited insight. Furthermore, the method is highly sensitive to the chosen number of planning steps.**\n\n**A2:** We thank the reviewer for the comment and address the concerns as follows:\n\n**(1) Main Gains Come from CoT Supervision, Not Receding-Horizon Planning.** \n\n- Receding-horizon planning is not the main source of improvement. As shown in Table R1 bellow (the original Table 4 in our paper), CoT-based supervision (Config A) yields a +6.2 SR / +5.6 SPL gain over the baseline, while receding-horizon planning alone (Config C) provides a smaller boost (+1.8 SR / +1.6 SPL). This confirms that CoT is the key contributor, with horizon planning playing a complementary role.\n\nTable R1: The **original Table 4** in our paper for ablation study on different components.  A: CoT Reasoning, B: Non-CoT Reasoning, C:Receding-Horizon Action Planning.\n| **A** | **B** | **C** | **NE↓** | **OSR↑** | **SR↑** | **SPL↑** |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| | | | 7.78 | 43.7 | 35.1 | 30.2 |\n| √ | | | 7.08 | 47.6 | 41.3 | 35.8 |\n| √ | √ | | 6.92 | 49.1 | 44.2 | 38.9 |\n| √ | √ | √ | **6.01** | **52.2** | **46.0** | **40.5** |\n\n- To further clarify their individual contributions, we have added new ablation experiments isolating each component. Results show that both modules independently contribute to performance improvements, supporting their complementary roles in enhancing reasoning and planning (see the following table).\n\nTable R2: Updated ablation study isolating each component. A: CoT Reasoning, B: Non-CoT Reasoning, C:Receding-Horizon Action Planning.\n| **A** | **B** | **C** | **NE↓** | **OSR↑** | **SR↑** | **SPL↑** |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| | | | 7.78 | 43.7 | 35.1 | 30.2 |\n| √ | | | **7.08** | **47.6** | **41.3** | **35.8** |\n| | √ | | 7.12 | 46.3 | 40.6 | 35.7 |\n| | | √ | 7.14 | 47.3 | 37.1 | 32.2 |\n\n\n**(2) Multi-Step Prediction Is Non-Trivial and CoT-Aligned.**\nWe respectfully argue that predicting the next n actions is a non-trivial task.\n- Multi-Step Prediction leverages the planning capabilities offered by the CoT-based auxiliary supervision (**Line 270-272**). CoT reasoning is inherently future-oriented, and multi-step prediction is a natural structural extension. It encourages the model to plan ahead, align with temporally abstract supervision in CoT-based auxiliary task, and improve short-term forecasting and consistency (**Line 191**).\n- Empirically, moderate-length horizons yield coherent behaviors that single-step prediction cannot easily capture.\n\n**(3) Horizon Length Is Not a Sensitivity Issue.**\nWe do not view performance variation with horizon length as a sign of fragility. \n- As shown in Table 5, performance varies smoothly, and a range of reasonable values provide consistent gains. \n- We treat horizon length as a standard design choice that balances short-term reactivity with long-term planning, not as a sensitive hyperparameter. \n\nWe will clarify this in the revision.\n\n\n**Q3. Unclear CoT vs. pre-think distinction: A major open question is: What are the fundamental differences between the CoT-based reasoning task and the pre-reasoning (pre-think) scheme?**\n\n\n**A3:** Thanks for the question. The fundamental distinction lies in how and when CoT is used:\n\nOur CoT-based reasoning is used only during training as an auxiliary task that generates reasoning sequences without actions to enhance navigation reasoning ability. During inference, our model only predict actions without CoT Reasoning.\nPre-Think, in contrast, jointly generates both reasoning and actions in training and inference, and uses the actions conditioned on the CoT directly for inference, which leads to Inference-time Reasoning Collapse.\n\nWe further provide other detailed differences from multiply aspects:\n- Loss function. For Aux-Think, the training loss function is Equation 5 and the loss function of CoT-based auxiliary task is **Equation 2**. For Pre-Think, the loss function is the Pre-Think branch of **Equation 1**.\n\n- Training-only vs. Training + Inference.\nCoT supervision in our method shapes internal representations during training, while inference remains clean and direct. Pre-Think depends on CoT generation at inference time, making it more prone to reasoning collapse and less efficient, as demonstrated in Table 3 in our paper.\n\n- We also provide a quantitative visualization in **Figure 2** in our paper that contrasts Pre-Think and the CoT reasoning in Aux-Think. \n\nWe hope the explanation and accompanying analysis help address the reviewer’s problem.\n\n**Q4: Dataset construction details: The authors omit crucial details about how the R2R-CoT-320k dataset was constructed in the main paper, instead hiding them in the supplementary material.**\n\n**A4:** Thanks for the suggestion. We have included key information about dataset construction in Section 3.2 (Line 133-140), including the simulator, VLM model, CoT format, and dataset scale. Given space limits, we prioritized a concise description of the dataset in Section 3.2, while allocating the remaining space to the Aux-Think framework and empirical analysis. We agree that the dataset is also the key contribution of our work and this section can be better highlighted, which will be revised in the final version accordingly."}, {"review_id": "hbTMXjhntM", "reviewer": "Reviewer_FdAS", "summary": "This paper explores the integration of reasoning into VLN tasks. The findings reveals the inference time reasoning harms the model performance, and a reasoning-aware co-training strategy is proposed to internalize the reasoning ability and evaluated on R2R val-unseen split.", "questions": "1 how are the retrospective thinking data labelled?  Do these data contain self-correct information?\n\n2 In the table 1, it's still not very clear how many training data are added. How many tokens are the 600k training extra data? I would suggest using tokens to represent the amount of data when compared to other methods.  \n\n3  How to check the quality of Qwen data? It's not very convincing to demonstrate the pre-think and post-think performance (Finding 1 in line 157 - line 158)", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1 The whole paper is well strcutred and layout is clear\n\nWeakness  \n\n1 A dataset is listed as contribution but the introduction details and stats about the dataset is not enough.   \n\n2 The experimental results may not be stastically significant.", "was_revised": true, "final_justification": "I have read the responses by the authors and other reviewers' discussions. The author cleared my concerns about the data amount and quality. The SPL result of RxR CE task is still not convincing enough given two times of Aux-think data. I hope the authors can provide explanation on this in their revision. I would like to keep my original rating.", "rebuttal": "**Q1: The details of thinking data construction**\n\n**A1:** Thank you for the question. \n- We clarify that the construction process for our retrospective thinking dataset is detailed in **Appendix A.2 (see Lines 502–508)**. Specifically, we prompt the Qwen-VL 2.5 (72B) model with the navigation instruction, historical visual frames, and the ground-truth action sequence, and explicitly ask it to produce a deep reasoning trace for the next navigation steps, including reflecting on past decisions and future plans.\n\n- Importantly, we observe that the generated thinking traces often include self-corrective signals, such as identifying navigation mistakes and proposing better alternatives—indicating a form of retrospective reflection. This reflective property helps guide the model toward more robust policy learning.\n\n- We agree that this dataset construction process is an important part of our contribution. In the revised version, we will move more details from the appendix to the main paper (Section 3.2) and provide additional statistics and examples to better present its value.\n\n**Q2. Amount of training data at the token level**\n\n**A2:** Thank you for the detailed suggestion. \n\n- For simulation-based training data, our collection from R2R-CE and RxR-CE follows a similar procedure to UniNavid [1] and NaVILA [2], ensuring comparability in terms of both sample count and token quantity.\nWe agree that reporting the number of training tokens provides another more standardized basis for comparison.\n\n- We have recalculated the total token count using the Qwen tokenizer. Our additional 600k training samples contain approximately **60M text tokens** and 4.2M images in total. In contrast, based on the data sources described in the NaVILA paper, we estimate that NaVILA’s training data amounts to approximately **200M text tokens** and 9.7M images. NaVILA uses a large amount of general VQA data (1000K) and real-world navigation videos (500K). The general VQA data in NaVILA introduces a large number of extra tokens and images that are not fully aligned with the navigation task, so we estimate the tokens according to the origin papers of the datast. In conclusion, the number of training tokens in NaVILA is much larger than ours.\n\n- Despite using significantly fewer training tokens, our method achieves comparable performance, which further supports our contribution of data efficiency through CoT-based auxiliary supervision. We will include the token counts and clarify this comparison in the revised version.\n\n[1] Zhang, J., Wang, K., Wang, S., Li, M., Liu, H., Wei, S., ... & Wang, H. (2024). Uni-navid: A video-based vision-language-action model for unifying embodied navigation tasks. arXiv preprint arXiv:2412.06224.\n[2] Cheng, A. C., Ji, Y., Yang, Z., Gongye, Z., Zou, X., Kautz, J., ... & Wang, X. (2024). Navila: Legged robot vision-language-action model for navigation. arXiv preprint arXiv:2412.04453.\n\n**Q3. The quality of Qwen data**\n\n**A3:** Thank you for the question. \n\n- We assessed the quality of Qwen-VL 2.5 (72B)-generated CoT data using standard human evaluation methods, including checking alignment among instructions, actions, and reasoning steps, as well as assessing the coherence of the reasoning itself. Our qualitative analysis suggests that the reasoning data are generally reliable and suitable for use in navigation tasks. Therefore, we used the Qwen-generated CoT data without any modification.\n\n- While we did not perform extensive filtering or refinement, our experimental results offer indirect evidence of quality: Aux-Think, trained on these unmodified data, achieves state-of-the-art performance and strong data efficiency. This suggests that the Qwen-generated content is well suited for training.\n\n- We will release our dataset to support further community efforts in analyzing, evaluating, and improving CoT data quality for navigation tasks.\n\n**Q4.The experimental results may not be stastically significant.**\n\n**A4:** Thanks for the question. We provide the additional scalability experiment by increasing the total training size from 60M text tokens to 130M text tokens to address your concern about the experimental results. \n\nTable R1: Updated experimental results on R2R-CE benchmark\n| R2R-CE |  Version|Training Tokens|Data Size  | NE ↓   |  OSR ↑   | SR ↑   | SPL ↑ | \n| ------------| -------------- | -------------- | -------------- | -------- | ------------ | -------- | ------- |\n| NaVILA  | -| 200 Million  |3.1 Million | **5.22**    | 62.5   | 54.0   | **49.0** |\n| Aux-Think | Initial Submission| 60 Million  |0.9 Million  | 5.88 |54.9         |49.7    | 41.7   |\n| Aux-Think | Update in Rebuttal| 130 Million  |1.9 Million  |5.49  | **62.9**  |**55.7** |48.7  |  \n \nTable R2:  Updated experimental results on RxR-CE benchmark\n| RxR-CE  |  Version |Training Tokens |  Data Size | NE ↓   |  OSR ↑   | SR ↑   | SPL ↑ | \n| ------------| -------------- | -------------- | -------------- | -------- | ------------ | -------- | ------- |\n| NaVILA  | -| 200 Million|3.1 Million | 6.77    | -              | 49.3   | **44.0**   |\n| Aux-Think | Initial Submission| 60 Million|0.9 Million  | 6.91 |60.1         |49.3    | 37.9    |\n| Aux-Think | Update in Rebuttal | 130 Million|1.9 Million  | **6.40**  | **63.9**  |**52.4** |39.9    |\n\nWe observe that Aux-Think, despite using significantly less scale-up data, achieves state-of-the-art performance on the most important metric (Success Rate) and remains comparable to current SOTA models on other metrics. These consistent results across multiple benchmarks demonstrate the significant effectiveness and data efficiency of our method."}, {"review_id": "irkVoygvRs", "reviewer": "Reviewer_7TT2", "summary": "This paper addresses an important and timely question in vision-and-language navigation: how can embodied agents handle long-horizon tasks more effectively? The authors propose using Chain-of-Thought (CoT) reasoning as a promising direction and provide a thorough analysis of how to incorporate CoT into the learning process. The main contribution is Aux-Think, a method that uses CoT during training but not at inference. The approach is well-motivated and supported by experiments demonstrating its effectiveness and data efficiency.", "questions": "I believe the most important addition would be a real-world demonstration on a long-horizon task, as real-world performance is ultimately the key concern in robotics. As for the remaining points, it would be helpful if the authors could offer some further discussion or exploration; including experimental evidence would be even better.", "limitations": "The authors provide a discussion of the method's limitations, which I think reasonable and fair.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1、The paper addresses a critical issue in VLN—how to incorporate structured reasoning like CoT into long-horizon embodied tasks. The proposed perspective is promising, valuable, and underexplored, especially given the known benefits of CoT in static tasks.\n\n2、The authors provide a well-articulated conceptual motivation by connecting human dual-process theory to the proposed Aux-Think strategy. The analogy to System 2 (explicit reasoning during training) vs. System 1 (implicit execution during inference) is not only intuitive but also inspired in the context of robot learning.\n\n3、The paper presents a thorough and clearly defined comparison of four reasoning strategies (No-Think, Pre-Think, Post-Think, Aux-Think). This taxonomy may inspire future work that revisits the role of reasoning in policy learning.\n\n4、Aux-Think achieves state-of-the-art or competitive performance on standard VLN-CE benchmarks, especially under limited data conditions. The success rates on long-horizon tasks further support the claim that reasoning-guided training improves generalization and stability.\n\nWeaknesses:\n\n1、One concern is that while the paper does include real-world experiments, they mostly cover short-range tasks. Since the method is motivated by long-horizon reasoning, it would be helpful to see results on longer and more complex real-world instructions to better assess its applicability outside simulation.\n\n2、In Section 3.3, the authors attribute the failure of Pre-Think and Post-Think to hallucinated or unstable CoT predictions in long trajectories. This is convincing, but it raises the question: could techniques like DAgger or student forcing be used to make the model more robust under distribution shift, instead of avoiding inference-time CoT altogether? Some exploration in this direction would strengthen the analysis.\n\n3、The two auxiliary tasks—Instruction-based Reasoning and Receding-Horizon Action Planning—are introduced without much explanation and discussion. While they improve results (Table 4), the paper does not make clear why these tasks were chosen or what role they play in learning better representations. Moreover, the ablation in Table 4 lacks a proper isolation, which makes it difficult to assess the true contribution of each component.\n\n4、(Minor point) Since the CoT supervision comes entirely from LLM-generated text (Qwen-VL 2.5), it would be good to say something about quality control. Even a brief mention of filtering or checking would help make the results more convincing.", "was_revised": true, "final_justification": "This is a fairly good piece of work with no obvious flaws or shortcomings. I believe it meets the acceptance bar for NeurIPS, and therefore I am keeping my original score.", "rebuttal": "**Q1. More complex real-world experiments**\n\n\n**A1:** Thank you for this insightful comment. We agree that evaluating on longer and more complex real-world instructions is important for thoroughly assessing the applicability of our method. We have actively scaled up real-world tests to cover longer-horizon tasks. We will include additional results and videos in the final version to better showcase performance under longer instructions and more challenging scenarios.\n\n**Q2: Other methods for avoiding inference-time CoT**\n\n\n**A2:** Thank you for the thoughtful suggestion and deep understanding of the task. Techniques such as DAgger or student forcing may offer alternative ways to mitigate distribution shift under inference-time reasoning. Their potential to reduce hallucination and improve stability in generated CoT for long-horizon navigation is worth investigating. \n\nAfter further reflection, we realize that these approaches could be meaningfully integrated with our framework in several ways:\n\n- DAgger. DAgger could be used to iteratively collect trajectories where the CoT-guided policy deviates from optimal behavior, and correct them using ground-truth supervision. This could help reduce hallucinations in Pre-Think and stabilize reasoning across long sequences.\n\n- Student Forcing. Student forcing could potentially be used during Aux-Think training to expose the model to its own predictions during intermediate CoT generation, making the auxiliary supervision more realistic and robust to compounding errors, even though CoT is not generated at inference time in our approach.\n\nWe appreciate the reviewer’s suggestion for highlighting these possibilities. These hybrid strategies open up valuable new directions that are complementary to our current design, which could be explored in the future.\n\n**Q3. Further Explanation for Instruction-based Reasoning and Receding-Horizon Action Planning**\n\n\n**A3:** Thank you for the detailed review.\n\n-  Instruction-based Reasoning.\nThis auxiliary task requires the model to reconstruct the instruction text based on the visual input from a trajectory. It strengthens the grounding between visual observations and language instruction, helping the agent better associate perception with task-level goals. Such grounding is especially important in VLN, where understanding the full instruction context is critical for accurate long-range navigation.\n\n- Receding-Horizon Action Planning.\nInspired by the future-oriented nature of CoT supervision, this task trains the model to predict a sequence of upcoming actions rather than just the immediate next step. This encourages long-term planning behavior and helps the model internalize structured, temporally abstract planning patterns, which are often implicit in CoT traces.\n\n- New Ablation Results.\nTo further clarify their individual contributions, we have added new ablation experiments isolating each component. Results show that both modules independently contribute to performance improvements, supporting their complementary roles in enhancing reasoning and planning (see the following table).\n\nA: CoT Reasoning, B: Non-CoT Reasoning, C:Receding-Horizon Action Planning.\n| **A** | **B** | **C** | **NE↓** | **OSR↑** | **SR↑** | **SPL↑** |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| | | | 7.78 | 43.7 | 35.1 | 30.2 |\n| √ | | | 7.08 | 47.6 | 41.3 | 35.8 |\n| | √ | | 7.12 | 46.3 | 40.6 | 35.7 |\n| | | √ | 7.14 | 47.3 | 37.1 | 32.2 |\n| √ | √ | | 6.92 | 49.1 | 44.2 | 38.9 |\n| √ | √ | √ | **6.01** | **52.2** | **46.0** | **40.5** |\n\n\n**Q4: The Quality Control of the CoT Data.**\n\n**A4:**\nThank you for the question. \n- We assessed the quality of Qwen-VL 2.5 (72B)-generated CoT data using standard human evaluation methods, including checking alignment among instructions, actions, and reasoning steps, as well as assessing the coherence of the reasoning itself. Our qualitative analysis suggests that the reasoning data are generally reliable and suitable for use in navigation tasks. Therefore, we used the Qwen-generated CoT data without any modification.\n\n- While we did not perform extensive filtering or refinement, our experimental results offer indirect evidence of quality: Aux-Think, trained on these unmodified data, achieves comparable  performance with the state-of-the-art methods and strong data efficiency. This suggests that the Qwen-generated CoT content is well suited for training.\n\n- We will release our dataset to support further community efforts in analyzing, evaluating, and improving CoT data quality for navigation tasks."}]

Author rebuttal:

Meta-review: This paper studies the role of inference-time reasoning in vision-language navigation tasks, where existing approaches for inference-time reasoning actually degrade the performance of VLN systems. To address this, the authors propose to train VLN systems with chain-of-thought at training time as auxiliary supervision, but perform inference without chain-of-thought, and find that it performs better than existing reasoning approaches for VLN.
Decision: Accept (poster)