CompassNav: Steering From Path Imitation to Decision Understanding In Navigation
Authors: LinFeng Li
Jian Zhao
Yuan Xie
Xin Tan
Xuelong Li
Venue: ICLR 2026 Poster (2026)
Keywords: Embodied AI
Goal-Driven Navigation
Large Vision-Language Models
Reinforcement Fine-Tuning
Primary area: applications to robotics, autonomy, planning
TL;DR: We shift navigation from "Path Imitation" to "Decision Understanding." With a novel, densely-annotated dataset and a gap-aware reward, our 7B agent learns to evaluate all moves, achieving SOTA results that surpass even larger models.

Abstract: The dominant paradigm for training Large Vision-Language Models (LVLMs) in navigation relies on imitating expert trajectories. This approach reduces the complex navigation task to a sequence-to-sequence replication of a single correct path, fundamentally limiting the agent's ability to explore and generalize. In this work, we argue for and introduce a new paradigm: a shift from Path Imitation to Decision Understanding. The goal of this paradigm is to build agents that do not just follow, but truly understand how to navigate. We materialize this through two core contributions: first, we introduce Compass-Data-22k, a novel 22k-trajectory dataset.Its Reinforcement Fine-Tuning (RFT) subset provides a panoramic view of the decision landscape by annotating all feasible actions with A* geodesic distances. Second, we design a novel gap-aware hybrid reward function that dynamically adapts its feedback to decision certainty, shifting between decisive signals for optimal actions and nuanced scores to encourage exploration. Integrated into an SFT-then-RFT recipe, our CompassNav agent is trained not to memorize static routes, but to develop an internal ``compass'' that constantly intuits the direction to the goal by evaluating the relative quality of all possible moves. This approach enables our 7B agent to set a new state-of-the-art on Goal navigation benchmarks, outperforming even larger proprietary models, and achieve robust real-world goal navigation on a physical robot.

Published as a conference paper at ICLR 2026
COMPASSNAV.STEERING FROM PATH IMITA- TION TO DECISION UNDERSTANDING IN NAV- IGATION
LinFeng Li Jian Zhao2.3+ Yuan Xie
Xin Tan't Xuelong Li
East China Normal University
The Institute of Artificial Intelligence (TeleAI), China Telecom
3 Northwestern Polytechnical University
11 llfengestu. ecnu. edu. en, (xtan, yxie @cs. ecnu. edu. en Bellong_li0ieess
zhaoj 900chi natel maatllee wlleoom.cc
ABSTRACT
The dominant paradigm for training Large Vision-Language Models (LVLMs) in navigation relies on imitating expert trajectories. This approach reduces the complex navigation task a sequence-ro-sequencc replication of single cor- rect path, fundamentally limiting the agent's ability to explore and generalize. In this work, argue for and introduce new paradigm: shift from Path Imi- tation to Decision Understanding. The goal of this paradigm is to build agents that do not just follow, but truly understand how to navigate. We material- ize this through two core contributions: first, we introduce Compass-Data-222 novel 22k trajerajyy dataset. Its Reinforcement Fine- Fine-Tuning (RFT) subset pro- vides panoramic view of the decision landscape by annotating all feasible ac- tions with A geodesic distances. Second, we design a novel gap-aware hybrid reward function that dynamically adapts its feedback to decision certainty, shift- ing between decisive signals for optimal actions and nuanced scores to encourage exploration. Integrated into an SFT-then-RFT recipe, CompassNav agent is trained not to memorize static routes, but to develop an internal "compass" that constantly intuits the direction the goal by evaluating the relative quality of all possible moves. This approach enables our 7B agent to set a new state-of-the-art Goal navigation benchmarks, outperforming even larger proprietary models, and achieve robust real-world goal navigation on physical robot. Project page: https: /tti::////innnssss github. io/ CompassNav
INTRODUCTION
The goal of Embodied AI extends beyond building agents capable of robust, autonomous operation within complex, previously unseen environments; it aims to create collaborative partners capable of interpreting human intent and processing abstract natural language instructions. A key benchmark for this capability is Goal-Driven Navigation (Chaplot et al., 2020; Krantz et al., 2022). Unlike Vision-and- Exsion-and-Langee LangLage Navigation (VLN) (Anderson al., 2018; Zhao et al., 2026), which provides dense, step-by-step instructions, goal-driven navigation presents agents with sparse, high-level goals (e.g., "find chair"). This requires the agent to explore effectively and reason spatially under uncer- tainty, without explicit guidance.
Approaches to this problem generally fall into Modular or End-to-End (E2E) categories. Traditional modular systems rely on explicit world representations (Zhou et al., 2025; Yokoyama et al., 2024a). While geometrically precise, they face two main challenges in real-world deployment: fragility to sensor noise and a semantic gap where engineered maps struggle to interpret unstructured human
'These authors contributed equally to this work.
1 "orresponniig authors.

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Published as a conference paper at ICLR 2026
<Thinking.. ? choose Path
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o) Our Decision Understonding
Thinking >> choose Path
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Waypoint Decision Point
B) Others: Poth Imitation
Figure 1: Visualization of the shift from Path Imitation Decision Understanding. (Bottom Left) The prevailing Path Imitation paradigm treats navigation as replicating a single expert path (solid line), penalizing alternative choices. (Top Left) contrast, our Decision Understanding paradigm teaches the agent to evaluate the relative value of all alternative paths (dashed lines), enabling flexible judgment at decision points. (Right) A concrete instantiation of these concepts a navigation scenario.
commands (e.g., "find a quiet spot for reading"). In contrast, we advocate for E2E approaches (Goet- ting al., 2024; Gu et al., 2025) based on Large Vision-Language Langugge Models (LVLMs). LVLMs avoid the reliance on fragile explicit mapping and possess a native capacity for processing natural lan- guage, making them well-suited for understanding complex human intent.
However, despite the potential of LVLM architectures, current training paradigms often rely on Path Imitation (Liu et al., 2025b; Gao et al., 2025; Krantz et al., 2020). This approach treats navigation as memory task, optimizing the agent to minimize deviations from single expert trajectory. We argue that this suboptimal for dynamic environments where valid paths are rarely unique. Strictly imitating single ground truth ignores alternative viable routes and fails to teach the agent the underlying causal structure of ceciattoonn-feeiivvellyyy prioritizing memorization over reasoning.
To address this, introduce paradigm shift towards Decision Understanding, We define this as the agent's ability to internalize spatial structures and execute a Think-then-Act heuristic. Unlike the binary signals used in path imitation, decision understanding requires the agent analyze the current state, predict the outcomes of all available actions, and make decisions based on a panoramic value assessment. This enables the agent to learn just what do, but why one action preferable to another. We visualize this shift in Figure 2.
We implement this vision in the CompassNav framework, which is built First, the Compass-Data-221 dataset moves beyond single-path supervision. Its RFT subset utilizes A* geodesic distances to annotate all feasible actions, creating dense field of correctness within the decision space. Second, we design Gap-Aware Hybrid Reward function to balance certainty and exploration. By analyzing the gap between optimal and suboptimal actions, the function enforees decisive strategies in clear scenarios while allowing for higher entropy in ambiguous ones, thereby adapting to the multi-path nature of real-world navigation.
technical pillars.
Finally, to ensure our agent is prepared to develop this sophisticated reasoning capability, we address the "cold-start" problem (Zhang et al., 2025b) with a preparatory Supervised Fine-Tuning (SFT) stage. This entire EFT-then-RFT recipe provides an integrated solution that operationalizes our new paradigm, enabling agents to learn how to weigh options and decide, rather than just follow. Our core technical contributions are:
The Compass-Data-22 Dataset for Decision Understanding. We introduce a novel dataset of 22k trajectories designed to support our new paradigm. It includes distilled rea- soning traces for Supervised Fine-Ture Tuning (SFT) and a Reinforcement subset that shifts supervision from single-path imitation panoramic view, densely an- notating all feasible actions with geodesic distances.
(RFT)
A Gap-Aware Hybrid Reward for Nuanced Policy Alignment.
We design a reward function that dynamically adapts feedback based on decision certainty. It handles ambiguity
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b) Modular Gool Novigation
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Figure 2: A conceptual comparison of embodied navigation paradigms.
using relative scores while providing decisive signals in clear-cut scenarios, effectively cultivating decision- making skills by leveraging the dense annotations in Compass-Diie -ata- 22k.
2
RELATED WORK
Modular End-to-End Navigation. Goal-driven navigation has traditionally been tackled by modular pipelines that strictly separate perception, mapping, and planning (Yokoyama et al., 2024a). Systems like CogNav (Cao et al., 2024) and UniGoal (Yin et al., 2025) rely on constructing explicit, engineered world representations such as SLAM-based metric maps or topological graphs to achieve high geometric precision. However, this reliance on explicit mapping introduces signifi- cant vulnerability in real-world deployment. Modular pipelines often suffer from error propagation, where sensor noise and pose estimation drift degrade the integrity of the constructed map, alongside the high engineering complexity required to integrate disparate modules.
Conversely, present End-to-Enn approaches utilizing LVLMs aim to map observations directly to actions. Early attempts leveraged powerful closed-source models (e.g., GPT-4o) for zero-shot navi- gation (Zhang al., 2025a; Goetting et al., 2024). While demonstrating impressive reasoning, their prohibitive cost and latency render them impractical for real-time control. Alternatively, fine-tuning open-source LVLMs (av-.. Nav-R1 (Liu et al., 2025b)) has been hampered by a supervision granular- ity mismatch. Existing datasets (e.g., R2R) provide only single ground-truth paths, forcing models into a rigid path imitation paradigm that lacks the counterfactual data essential for robust decision- making. CompassNav overcomes these limitations by employing novel training paradigm that distills robust spatial reasoning and decision-making capabilities directly into open-source LVLMs. This approach not only surpasses the navigation performance of expensive proprietary models but also contributes cost-effective solution to the open-souree community, significantly lowering the barriers deploying advanced embodied an intuitive comparison of these concepts is visu- alized in Figure 2.
Reward Design for Reinforcement Fine-Tuning Effective RFT depends on reward quality. Sparse rewards (Yang et al., 2024) are intractable for long horizons, while simple dense heuristics (Luo et al., 2025) (e.g., Euclidean distance) fail to account for obstacles. Similarly, standard preference methods (Rafailov et al., 2024) rely on binary signals that miss the nuance of magnitude and am- biguity in decision-makiin
Consequently, current navigation RFT methods (Liu et al., 2025b; Qi et al., 2025) remain confined to restrictive imitation paradigm, rewarding fidelity single ref- erence trajectory. This approach reinforces rigid path replication rather than fostering the genuine decision understanding required for multi-path navigation.
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3 THE COMPASS-DATA DATASET: GENERATION AND FORMULATION
We frame goal-driven navigation sequential decision-making problem. At each step, an agent observes its current view, is presented with a set of feasible candidate actions, and must select one to execute (further details are in Appendix B). This section details the generation and formulation of our novel dataset, Compass-Dar Data, which is specifically designed to shift the training paradigm from path imitation decision understanding. as illustrated in Figure 3.
3.1
DENSE ANNOTATION FOR DECISION UNDERSTANDING
Existing VLN datasets, such as R2R (Anderson et al., 2018) and RxR (Ku et al., 2020), are ill-suited for this purpose due inherent structural limitations. Beyond the goal-suitability miere trajectory endpoints are defined by language constraints rather than object proximity tthee datasets suffer from a critical supervision- appervsionnggaauulrigg granularity mismatch. By isolating a single ground-truth path with- out annotating alternatives, they constrain agents to rigid imitation. This lack of dense, comparative feedback prevents the model from evaluating the relative quality of the full action space, necessary condition for learning genuine pacision-making logic.
To facilitate "Decision Understanding." we constructed the Compass-Data-22 22k dataset, directly ad- dressing the supervision scarcity inherent in existing benchmarks. Our generation pipeline trans- forms the training signal from a sparse optimal path into a panoramic value assessment (Fig- ure 3). Specifically, the pipeline employs Action Proposal Module (APM) (more details in Ap- pendix C.1) that utilizes real-time depth maps and occupancy grids to identify all feasible candidate actions, discretizing them into polar vectors (r; O). Distinct from traditional methods that only an- notate the single best move, our Oracle A Annotator leverages global simulator information to calculate shortest geodesic distances for every candidate action relative to the goal. This produces a complete action-value vector at each timestep, effectively mapping the gradient of correctness across the decision space. To further enhance data diversity, we incorporate Backtracking Mechanism; rather than strictly adhering to the shortest path, the agent identifies Ambiguous Points states with multiple viable options and actively retraces its steps explore and record alternative trajectories. This results in dense, panoramic supervision that teaches the agent the relative quality of all potential choices.
RFT Data Structure. After generation, this densely annotated data is structured for the Reward Fine- Fine-Tuning stage. To maintain a consistent structure throughout our framework, we establish a standard data format. As shown in Figure 3 (top right), each RFT data sample consists of a standard input (the instructional prompt and the agent's current visual observation) paired with a specialized target for reward modeling. This target contains the optimal action's 's ID and the complete vector of A distances for all candidate actions at that step. This vector provides the fine- grained preference signal that is essential for our gap-aware hybrid reward function and the GRPO framework.
3.2 COMPASS- -ANSS--AAAASSSAAA KNOWLEDGE DISTILLATION FOR POLICY INITIALIZATION
Initializing RFT directly from base LVLM is often inefficient due to the "cold-start" prob- lem (Zhang et al., 2025b). A base model's initial policy typically poor, leading to suboptimal actions and sparse reward signals that hinder learning. To address this, we introduce a preparatory Supervised Fine-Tunme Tuning (SFT) stage using our Compass- Saapaass-DttaSFFP1 Pata-SFF-11k set, which is designed to instill foundational "reason-ther then-act" capability.
Instead of constructing host reasoning for a predefined path, we adopt a more ecologically valid knowledge distillation strategy. We task powerful teacher model, Qwen-QvQ evenn-QQ (Team, 2024), to genuinely perform the ObjectNav task in habi tat-s i.. By recording the complete, step-by-step reasoning and action choices only from its successful episodes, we form an SFT dataset that reflects emergent and effective exploratory strategics.
SFT Data Structure. As shown in Figure: 3 (bottom right), each SFT training instance shares the same input structure as the RFT data: (a) an instructional prompt and (b) the agent's current visual observation. Its distinction lies in the target output, which is a single string containing the teacher's full cognitive process and decision, formatted as This format explicitly
<think>. wwt.ooiiinn....hhhinkeeeee.

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Published as a conference paper at ICLR 2026
1) Data Generation Pipeline
2) Compass- Saanpass-DDta-RRe Data-RFT-11K
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Figure 3: Overview of our data generation and formulation pipeline. (I) Data Generation: We use an A planner in habitat-Se -ii to densely annotate all feasible actions with their distance the goal. (2) Dataset Formulation: Trajectories are formatted into two types. SFT data (bottom right) contains teacher's 's reasoning trace for imitation. RFT data (top right) contains the full vector of action distances for reward modeling.
trains the model externalize its reasoning before committing an action, establishing the foun- dational 'reason-then-act behavior. We intentionally exclude historical frames from the input to en- sure compatibility with external memory modules. This design choice is deliberate, as our empirical validation showed that current methods for integrating historical context directly into the training process, such as using text summaries or concatenated images, consistently led a degradation in performance (Details can be found in Appendix E.2).
4 COMPASSNAV FRAMEWORK
CompassNav is two-stage fine-tuning recipe designed to first initialize robust policy and then align with environmental objectives. We adopt this SFT-then-RFT structure to create a synergistic learning process. The initial SFT stage efficiently overcomes the "cold-start" problem by instilling strong policy prior through imitation. Subsequently, the RFT stage leverages this competent initial policy to align the agent towards genuine decision understanding. The entire process is visually summarized in Figure 4.
4.1 STAGE 1: POLICY INITIALIZATION VIA SUPERVISED FINE-TUNING
While our ultimate goal move beyond imitation, a foundational reasoning capability is prereq- uisite. Stage I thus employs a targeted form of imitation (SFT) to instill this beasonnthen--an struc- ture by learning from teacher model's reasoning process. At each timestep the model trained to generate two-part response that mimics the teacher's output: chain-of-thought rationale followed by a final action choice, formatted as <think>.. </think>
<answer>k < </answer>.
A key practical challenge is ensuring the model's 's action choice 'k' is always valid action from the set of available candidates Aç. To enforce this, we employ masked multiple-choice decoding. This is achieved by applying masked softmax over the decoder's 's's output logits z for the answer token, restricting the vocabulary to only the set of valid candidate indices V,:
exp(2;)
Te 21) Eyen, (xp)
for j V.
This simple and effective technique ensures that all generated outputs are executable, which is cru- cial for the stability of the subsequent RFT stage.
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Instruction
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Figure 4: The NompassNav two-stage training pipeline. In Stage (SFT), pretrained VLM is fine-tuned to imitate teacher's "reason-ther -aesnnnt output. In Stage 2 (RFT), the SFT-tumed policy generates multiple responses, which are scored by our Gap-Aware Hybrid Reward function.
The SFT loss a standard cross-entropy loss over the entire teacher-generated sequence, including both the reasoning tokens and the final action token:
Mel
LSFT (0) = lE (e)
log po (o, Ze; Ve.cu)
(2)
where Y/ represents the full target sequence <think>, k</answer>.
4.2 STAGE 2: POLICY ALIGNMENT WITH GAP-AWARE HYBRID REWARD TUNING
In Stage 2, we align the SFT-initialized policy with environmental objectives using the Group-wise Reward Policy Optimization (GRPO) (Shao al., 2024) framework, shown in Figure (Stage 2). The learning signal is provided by our novel gap-aware hybrid reward function.
GRPO Sampling and Reward Assignment. For given input prompt 2t, use the policy Ty to generate a group of G distinct output sequences, (31, 9/,.... }. For each generated sequence yj- parse it to extract the chosen action iy. The reward for this sequence, r(y,), then determined by the quality of this chosen action, evaluated using our gap-awart hybrid reward function and the pre-computed A distances for all candidates.
Gap-Aware Hybrid Reward Function. decisive signal in high-certainty scenarios while remaining nuanced encourage exploration in low-certainty ones. This achieved by composing the reward from two key components: continuous base score and vertainty-mouulan dynamic bonus.
The base score, St (6,) provides a continuous evaluation for all available options, is calculated using softmax function over the vector of distances to the goal, d,. Actions with shorter distances receive a higher score, reflecting their relative quality:
exp(- (85) /T)
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where 7 is a temperature byperparameter that controls the sharpness of the distribution.
The core of our adaptive mechanism lies in the dynamic bonus, which is governed by "certainty" factor, Gt- This factor dynamically assesses whether the current situation is decisive or ambiguous
by measuring the normalized gap between the best (d ) and second-best (d(")) options:
(
(11)
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This normalization allows the signal prioritize broad exploration when far from the goal and promote precision when near it. A high g. indicates clear, high-certainty choice, while a low g. signifies ambiguous one.

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Published as a conference paper at ICLR 2026
The final hybrid reward, r (85) combines the base score with the bonus, which is modulated by this certainty factor and is triggered only for the optimal action ;":
r(v)) = r (1,) + .max 9t Kig =
(5)
where i" arg ming d!4) is the optimal action, [+] the indicator function, and fmux is the preset maximum bonus ratio. The final reward value is then clipped to the range [0, 1].
To illustrate its superiority, we analyze its behavior against two common baselines- sparse Bi- nary reward (Rafailov et al., 2023) and linear normalized Min-Max reward (Patro & Sahu, 2015) across three key navigation scenarios in Figure 5.
A. Decisive Case
B. Ambiguous Case (1.00, 1.01, 1.03, 1.10]
C. Indistinguishable Case [1,0,10, 1.0, 1.0
[1.0, 4.0, 8.0)
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Figure 5: A comparative analysis of our Gap-Aware hybrid Reward against Binary and Min-Max schemes across three representative navigation scenarios. Each scenario shows the reward assigned for choosing the best, second- bestt and worst actions.
As shown in Figure 5, Gap-Aware hybrid reward function demonstrates superior behavior in diverse scenarios. In the Decisive Case (A), it creates large margin between the best and second- best actions (1.00 vs. 0.12), providing strong and unambiguous learning signal where baselines fail to differentiate. In the Ambiguous Case (B), it correctly assigns similar, non-extreme scores to closely-valued options, encouraging exploration rather than arbitrarily penalizing viable choice. Finally, in the Indistinguishable Case (C), while other methods output a misleadingly perfect score of 1.0, our function provides honest, low score of 0.25. This prevents the agent from receiving an illusory high reward for random guess, fostering more robust and faithful policy.
Objective Function. The GRPO objective maximizes the expected reward over the generated group. After normalizing the rewards to produce advantages A(y;), the loss function to be minimized is:
G
(6) Here, TSFT is the frozen reference policy from Stage I (the "Reference Model" in Figure 4), and the KL term regularizes the policy update. This objective encourages the policy generate sequences leading to high-reward actions, as evaluated by our fine-grained reward model.
(f) E. (u5)
A(y,) log Tz(3)(2)) + 8KL Ez,
EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Datasets and Tasks. We generate training data in habitat-sin using the HM3Dv2 (Yadav al., 2023) train split. To evaluate generalization, we test our agent on three challenging, unseen validation splits: HM3DvI-val (Ramakrishnan et al., 2021a), HM3Dv2-val (Yadav et al., 2023) and MP3D-val (Chang et al., 2017). These splits feature completely held-out scenes and object instances, ensuring rigorous evaluation of the agent's ability to navigate novel environments. The primary tasks are Object-Goal (Chaplot et al., 2020) and Instance- Ieage-mmae-oaaa Navigation (Krantz et al., 2022; Liu et al., 2025a). Further details on dataset statistics and task definitions are provided in Appendix B.
Evaluation Metrics. We report two standard metrics: Success Rate (SR) and Success weighted by Path Length (SPL). SR measures the rate of successful episodes, while SPL weights each success by the ratio of the optimal path length to the actual path taken.
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Table 1: Comparison with Modular goal-driven navigation methods. We categorize methods by whether they are End-to-End (E2E) and whether they utilize Memory (ME) (e.g., semantic maps, topological map).
Backbone Models
HM3D
MP3D
Method
Venue
E2E
ME
Vision
Text
SR
SPL
SR
SPL
Habitat-Web
CVPR'22
X
41.5
16.0
8.5
ESC
ICML'23
X
V
GPT-3.5
39.2
22.3
28.7
14.2
1.3VV
IROS 23
X
V
@ GPT-2
50.4
23.1
34,9
14.5
InstructNav
CoRL'24
X
V
$ GPT-V
LLaMA3-70B
50.0
20.9
PSL
ECCV'24
X
X
CLIP
42.4
19.2
18.9
6.4
VoroNav
ICML'22
X
V
BLIP
GPT-3.5
42.0
26.0
Pixel-Nav
ICRA'22
X
V
LLaMA-Adapter
GPT-4
37.9
20.5
VI.MM
ICRA'24
X
V
BLIP-2
52.4
30.4
36.4
17.5
GAMap
NeurIPS' 24
X
V
CLIP
GPT-4
53.1
26.0
SG-Nav
NeurlPS 24
X
V
LLaVA-1.6-7B
GPT-4
54.0
24.9
40.2
16.0
UniGoal
CVPR'25 25
X
V
LLaVA-1.6-BB
LLaMA-2-7B
54.5
25.1
41.0
16.4
CompassNav
ICLR'2R 26
X
Qwen2.5-VL-7B
56.6
27.6
17.5
Table 2: Comparison of CompassNav with various open-source and proprietary models. Our 7B parameter model is benchmarked against models of varying scales.
ObjNav
InsImageNav
AVG
Models
SR
SPL.
SR
SPL.
SR
SPL.
Open- source Models
Qwen2- VL-7B (Wang ct al., 2024a)
35.9
14.8
5.30
3.60
20.6
9.20
Qwen2.5 V-L-3B (Bai al., 2025)
25.8
10.3
8.70
3.20
17.3
6.80
LLama3.2- BBBBBBBBBBBBBBB (Dubey al., 2024)
25.8
8.4
3.00
2.50
17.1
5.50
Closed-Soune Models
GPTT-o (Hurst al., 2024)
52.4
23.5
29.8
41.1
18.4
Gemini-2.5-Flash (Comanici al., 2025)
50.0
10.6
24.1
9.70
37.1
10.2
GPT-o4-mini (OpenAI, 2025)
59.6
26.9
33.4
13.2
46.5
20.1
Our Models
Base Model (Bai ct al., 2025)
45.3
17.5
19.8
5.20
32.6
11.4
CompassNav (SFT)
54.1
23.1
23.9
7.90
39.0
15..5
CompassNav (SFT+RFT)
61.6
27.8
35.6
14.8
48.6
21.3
Implementation Details. CompassNav is built upon the open-source Qwen2.5-VL. (Bai et al., 2025) model. trained using our two-stage SFT-then-RIFT recipe. All detailed configurations, including specific training frameworks, hyperparameters, and hardware, are deferred to Appendix E.
5.2
MAIN RESULTS
To comprehensively evaluate CompassNav, we structure our comparisons two parts. First, benchmark against state-of-the-art modular methods, which represent the dominant approach in goal-driven navigation. Second, we compare with other end-to-end LVLMS to isolate the effective- ness of our training paradigm.
Comparison with Modular Navigation Methods. First, we benchmark our end-to-end Com- PassNav against state-of-the-art modular systems, with results presented in Table 1. These base- lines employ complex, multi-stage pipelines, often relying on explicit memory like semantic maps (Yokoyama et al., 2024a) history images. Instead of building explicit world model, we focus on distilling spatial reasoning capabilities directly into the model's parameters. We show that simpler agent achieves competitive, and often superior, performance, suggesting that focus on the learning paradigm itself is more direct and efficient path toward capable navigation agents.
Comparison with End-to-End LVLMs. To isolate the effectiveness of our framework from the base model's inherent capabilities, we conduct a comparison against other powerful LVLMs within the same end-to-end setting. As shown in Table 2, CompassNav agent significantly outperforms
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Table 3: Performance comparison on the NavNuances benchmark. We compare CompassNav
against GPT-4 based agents and the base model to evaluate atomic reasoning capabilities.
Method
DC
NU
LR
RR
VM
NavG
81.87
20.51
58,54
39,63
7.06
NavGPT-4
91.87
34.78
54.83
67.61
11.36
NavGPT-4V
92.68
39.13
62.87
56.25
13.64
Qwen2.5-VL-78
79.62
19,10
50.37
49.00
7.88
CompassNav-7B
82.54
23.85
59.92
59.31
22.94
these much larger, general- caneral-purpose models like GPT-4o (Hurst et al., 2024) and even surpasses o4- mini, a model renowned for its powerful general reasoning capabilities.
We conduct special comparison with Nav-RI (Liu al., 2025b), concurrent work that also fine- tunes an LVLM for embodied tasks. Nav-R1 aims to build a general-purpose agent and has achieved progress on multiple tasks. However, its training paradigm remains rooted in imitating a single expert path. On HM3D-OVON (Yokoyama et al., Jbnchhaknn,n,rk, our method surpasses Nav-R1 on both key metrics (See Figure 6 for details). Notably, we achieve it while using one-tenth of the training data and starting from general-purpont LVLM, rather than 3D-specialized model. This result strongly suggests that our paradigm is a more efficient and effective path.
Evaluation on Decision Understanding, While standard metrics like SR and SPL measure naviga- tion outcomes, they do not explicitly quantify the agent's 's spatial reasoning process. To validate our evaluate CompassNav
claim of decision understanding, et al., 2024b), which disentangles navigation into atomic capabilities: Direction Change (DC) for basic instructions, Numerical Comprehension (NU) for counting and sequential memory (e.g., second door), Landmark Recognition (LR) for fine-grained visual grounding, Region Recognition (RR) for semantic room understanding, and Vertical Movement (VM) for multi-floor 3D spatial reasoning (e.g.. "Go upstairs").
the NavNuances benchmark (Wang
As shown in Table 3, CompassNav demonstrates decisive gains in metrics demanding deep spatial reasoning. Most notably, we observe a ~33x improvement over the base model in VM, significantly outperforming even NavGPT-4V. This confirms that our training paradigm successfully imbues the agent with an understanding of structural connectivity and 3D reasoning, going beyond simple path imitation. While CompassNav significantly improves upon the base model across all metrics, a per- formance gap remains compared to GPT-4V in DC and NU. This is largely due to task alignment and model scale: NampassNav is optimized for goal-oriented exploration rather than the rigid instruc- tion following required by VLN (impacting DC). The lower NU score reflects known limitation of 7B-scale models: they are more prone to hallucinations in memory deeeoryddependent long-context tasks compared to larger foundational models.
5.3 ABLATION STUDIES
Efficacy of the SFT Stage. Our experiments first validate the necessity of proper policy initializa- tion.
As show in Table 4, attempting RFT directly from this base model yields only marginal 3.7 improvement in SR due to inefficient exploration from naive pol- icy.Applying RFT on this SFF-initialized model further improves performance by another 12.3, confirming the synergy of our two-stage approach.And recently, some methods only teach the model to output the action space of navigation tasks in the SFT stage. In difficult goal nav- igation tasks, this method actually deteriorates the effect.
Table 4: Efficacy of the SFT Stage.
Config.
SR
SPL
base Model
19.8
5.20
SET (Action only)
17.9
5.78
SFT (Full)
23.3
7.90
RFT (from Scratch)
23.5
6.95
RFT (from SFT)
35.6
14.8
Analysis of the Gap-Aware hybrid Reward Funetion,The quantitative results in Table 5b show that our Reward significantly outperforms others that only consider absolute correctness or relative distance. A deeper analysis, shown in Figure 6, reveals the principles behind its effectiveness. Our ward function is designed to be adaptive: should provide a strong, decisive signal in high-certainty scenarios while remaining nuanced to encourage exploration in low-certainty ones. Figure 6(left)

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Published as a conference paper at ICLR 2026
Table 5: Ablation studies on the components of our framework.
Max Bouns
SR
SPI.
Reward.
SR
SPI.
Temperature
SR
SPI.
B= 0.5
31.3
12.2
Binary
29.5
11.1
T=
33.2
13.3
B= 1.0
35.6
14.8
Min-Max
29.2
12.5
T=0.5
35.6
14.8
= 1.5
27.9
11.7
Ours
35.6
14.8
33.7
13.9
(a) Efficacy of the Max Bonus
(b) Analysis of the Reward Func
(c) Efficacy of the Temperature
visualizes the reward gap across different hyperparameters, our chosen parameters(==0.5, B=1.0)
achieve strong gap in high-certainty cases while maintaining moderate in low-certainty cases, and as show in Table 5a and 5c, this combination also achieved the best results.
Furthermore, the training dynamics Figure 6(right) illustrate critical insight. While the Binary and Min-Max reward models achieve deceptively high scores during training, this merely indicates success at a simplistic proxy task: perfectly mimicking the single best action. In contrast, our Gap- Aware hybrid Reward, though yielding a lower absolute score, provides a more meaningful signal by teaching the model to evaluate options. This fosters a more generalizable reasoning capability and demonstrates that reward function's s alignment with the true objective of robust navigation is more critical than the raw magnitude of its score.
Fiihh Carta Ih-0741
Certaiity (M-25TO)
2011
3:52
Reward
5.5
1033 1:51 1450 11. T-3
F:51 7-02 1-33 7x57 Pu15 1:11
150
123
160
Stop
Figure 6: Left heatmaps showing the reward gap between the best and second-best actions under High and Low Certainty scenarios. Right training reward curves for different reward functions.
CONCLUSION
In this work, we present CompassNav. a framework that successfully moves beyond the prevailing paradigm of Path Imitation towards more robust Decision Understanding approach in navigation. To enable this shift, we make two core contributions: the Compass-Data-- dataset, which offers panoramic, per-action supervision far exceeding single-path imitation, and novel Gap-Aware Hy- brid Reward function that provides adaptive feedback tailored to decision certainty. Integratee into SFT-then-RFT recipe, framework transforms 7B parameter LVLLM into expert navigator that establishes a new SOTA. It not only outperforms even larger proprictary models in simulation but also demonstrates robust performance in real-world deployment. Our findings paving the way for future research in low-cost, intelligent embodied agents.
ETHICS STATEMENT
We adhere to the ICLR Code of Ethics. Our Compass-Data Daaaasss-Dtaa2222 dataset is generated via habitat-st m using public scenes (e.g.. HM3D) without PII or human subjects. We distill knowl- edge from the open-source Qwen-Qve QvQ to ensure licensing compliance. Acknowledging potential architectural biases in the source data, commit to releasing all datasets and models to support transparent research in assistive robotics.
REPRODUCIBILITY STATEMENT
To ensure full reproducibility, release the Compass-Data-2 dataset, fine-tuned weights (LoRA), and all code for data generation, training, and physical deployment. Detailed hyperpa- rameters and implementation specifics documented in Appendix E.
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THE USE OF LARGE LANGUAGE MODELS (LLMS)
An LLM was used solely for refining text clarity and grammar. No Al tools were involved in research ideation, experimental design, or data analysis. The authors verify the accuracy of the final manuscript and assume full responsibility for its content.
ACKNOWLEDGMENTS
This work is supported by the National Natural Science Foundation of China (No.62302167, 62476224, U23A20343, W2521174); Shanghai Committee Science and Technology (No.25511103300 25511104302, 25511102700); Young Elite Scientists Sponsorship Program by CAST YESS20240780;
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A MORE RELATED WORK AND MORE EXPERIMETNS RESULTS
A.1 MORE EXPERIMENTAL RESULTS
Based on current similar work, Nav-RI was tested other obejet navigation benchmarks HM3D- OVON (Yokoyama et al., 2024b). We also conducted tests with the same setting on the val unseen split of this benchmark. The results shown in the table 6. Our method only used one tenth of their training data to train the original Qwen2.5-VL-7B, which still exceeded their model trained from 3D-RI. This further demonstrates the superiority of our method.
Table 6: Obejet Navigation results on HM3D-OVON val-unsenn
Val-Unseen
Method
SR
SPT.
Uni-NaVid (Zhang et al., 2024)
39.5
19.8
MTU3D (Zhu et al., 2025)
40.8
12.1
Nav-RI (Liu et al., 2025b)
42.2
20.1
CompassNav (Ours)
43.5
21.6
To address concerns regarding domain diversity and demonstrate robustness beyond photorealistic scans (HM3D/MP3D), we extended our evaluation to the A12-THOR (ProcTHOR) dataset (Deitke al., 2022). We utilized the Habitat-compatible version evaluate over 8,000 episodes in un- seen synthetic environments. These environments feature distinct "game-like" textures and layouts. presenting significant domain shift from our training data.
Table 7: Performance comparison on the A12-THOR (ProcTHOR) benchmark. This evaluates Sim- to-Sim generalization from photorealistic training (HM3D) synthetic testing environments.
Method
SR
SPL.
EmbCLIP
47.0
20.0
ProcTHOR (Deitke et al., 2022)
55.0
23.7
CompassNav (Ours)
58.1
32.8
As shown in Table 7, CompassNav significantly outperforms both the baseline (EmbCLIP) and the domain-specific ProeTHOR agent. Crucially, the massive improvement in SPL (+9.1%) indicates that our agent plans highly efficient paths even in unfamiliar visual domains. This strongly supports that our agent has learned transferable spatial logic rather than overfitting to specific visual styles.
More Hyperparameter Analysis. We further investigate the stability of our training recipe by analyzing two critical hyperparameters: the KL divergence strategy and the GRPO group sampling size.
Table 8: Hyperparameter sensitivity analysis on KL Divergence strategy and GRPO Sampling Size (K).
(a) Analysis of KL Strategy.
(b) Ablation on Sampling Size K.
Method
SR
SPL
Size
SR
SPL
Baseline (Base Model)
45.3
17.5
K
60.2
26.
Adaptive KL
61.5
26.0
K 5 (Ours)
61.6
27.8
Fixed KL (Ours)
61.6
27.8
K 7
61.8
26.0
As shown Table 8a, we explored an adaptive KL mechanism (kil_ sal_farget- = 0.5, kl_hhoooo horizon = 2560.0). However, this did not yield performance gains compared to our tuned Fixed KL. We hypothesize that in our specific GRPO setup combined with the Gap-Aware Reward, a fixed KL coefficient acts as more stable anchor to the SFT policy. This effectively prevents the policy from drifting too far during the initial unstable exploration phase, ensuring consistent convergence. We also evaluated the robustness of the group size K in the GRPO algorithm. As presented in Table 8b, performance is generally robust across values. We observe that 5 offers the optimal balance between computational efficiency and variance reduction.
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B STANDARD BENCHMARKS AND TASKS
Scene Datasets. Our experiments are primarily conducted on two large-scale, standard indoor navigation scene datasets: First, Habitat-Matterpot 3D (HM3D) (Ramakrishnan al., 2021b) is a large-scale dataset of 3D indoor environments, consisting of 1,000 high-resolution scans of resi- dential and commercial spaces. Our work utilizes the version split for the Habitat Challenge, with the t train n. split comprising 800 scenes and the val split containing 100 scenes. We report results on two semantic annotation versions: HM3Dv1 (Habitat Challenge 2022, using HM3D-SSmantics-- v0.1 (Ramakrishnan et al., 2021a)) and HM3Dv2 (Habitat Challenge 2023, using HMIDD-Semantics- v0.2 (Yadav al., (023)).Seconde Matterport3D (MP3D) (Chang et al., 2017) is another large- scale RGB-D D dataset featuring 90 building-scale scenes, composed of 10,800 panoramic views from 194,400 images.
Task Datasets. We evaluate our method several goal-driven navigation task datasets: Object- Nav (ON) (Chaplot et al., 2020) requires agents to find an object of a given category. We evaluate on both the vl version (2000 episodes, 20 scenes, categories) and the v2 version (1000 episodes, 36 scenes, 6 categories) from the Habitat Challenge; HM3D-OVON (Yadav et al., 2023) is a large-scale open-vocabulary ObjectNav benchmark that significantly expands the semantic range, featuring over 15k instances across 379 distinct object categories; Instance Image-Goal Nav (IIN) (Krantz et al., 2022) requires agents to find a specific object instance. The goal categories, including "Chair", "Bed", "Toilet", "Couch", "Plant", and "TV", are consistent with the ObjectNav task.
Task Definition. In our work, the agent receives a posed RGB-D video stream and must execute a new action a E A upon receiving new observation. A key distinction exists between our two main tasks: for IIN, the goal is specific object instance provided as a goal image; for ON, the goal is an object category provided text.
Our action space A consists of discrete set of high-level actions represented as arrows ren- dered directly onto the agent's observation. A low-level controller translates the chosen arrow into a sequence of primitive simulator actions: move mrov_foowwad (25em), Surn_leff (30%), and turn purn_right (30%). An episode is considered successful if the agent executes the stop action when it is within predefined radius 1-meter of the goal, in less than maximum of 500 steps.
To handle the stop action, we are inspired by multi- agent collaborative frameworks. Instead of cluttering the primary agent's visual input with an additional stop command overlay, we employ dedicated Stop Agent. This agent, a non-fine-tuned Qwen2.5-VL-7B (Bai et al., 2025) model, prompted at each step to decide whether the goal is sufficiently in view to terminate the episode. This decouples the navigation and stopping decisions, simplifying the task for the primary agent. The prompt for the Stop Agent please refer D.2
C DATA GENERATION PIPELINE
C.1 ACTION PROPOSAL MODULE (APM)
Following VLMNav (Goetting et al., 2024), we used the Action Proposal Module (APM), a critical component that translates raw depth sensor data into discrete set of high-level, e candidate actions for the LVLM to reason about. The process is designed to ensure actions are safe, visually distinct, and biased towards efficient exploration.
First, at each timestep, the APM leverages the depth image to compute the traversable area within the agent's field of view. Based on the furthest navigable distance for each angle 0, an initial, dense set of candidate actions, Amnital is generated. Simultaneously, we construct 2D voxel map using accumulated depth and pose information, marking all observed areas within 2-meter radius as explored.
To encourage systematic exploration and prevent redundant movements, we filter Anital based on the exploration map. For each action, we define an exploration indicator variable e;:
[1 if its destination region is unexplored e, ( its destination region is explored
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Phenatle seaee Map
Ainkal
Adinal Wap
Deph
Figure 7:
Action Proposal Module(APP) module workflow diagram
To build the final action set, Afinl. we prioritize actions leading to new territory while ensuring sufficient visual spacing for the VLM to discern between options. This is achieved in two-step filtering process. First, we greedily add unexplored actions (c; = 1) that maintain a minimum angular spacing of fg:
Atnal Arnal U ((0,, r:) |e; and 10; 0,1 z 0s, v(O,, E Anmal
To ensure the agent can still navigate within known areas and cover all directions, we then supple- ment Atnal with explored actions (e; = 0), subject to larger angular spacing AA > Ag:
Aimal Anmal U r(;; r:)
0 and 10; 0,l 2 0a, v(Oj, r5) E Añmal)
For safety, the radius of each action is clipped via r; < min ( Tr, mmax). If no navigable actions found, 180 turn action is added to handle edge cases where the agent might be stuck. The final set of actions is then rendered as arrows onto the agent's RGB observation(Figure 7).
To bridge the gap between the LVLM's high-level decision-making and the simulator's low-level execution, we implement a deterministic low-level controller (Gu et al., 2025). This controller translates the agent's chosen high-level action, parameterized a discrete sequence of primitive actions executable within the Habitat simulator. The primitive action space consists of move move_forwareeeee
polar coordinate tuple (r, 0), into
25m). turn left (30%). ) and tuur_uurnnggire (30%).
The translation process follows a standard two-phase "turn-then-move" sequence to ensure pre- dictable navigation outcomes:
1. Rotation Phase:
The controller first addresses the angular component, 0. The angle, given in radians, is converted degrees. The number of required 30 turns is calculated using the ceiling function to ensure the agent turns at least the specified amount. The direction of the turn is determined by the sign of 6: positive value corresponds turn turn_leftt left, and negative value corresponds to turn_right.
2. Translation Phase: After completing the rotation, the controller handles the distance com- ponent, The number of 0.25m forward steps is similarly calculated using the ceiling function. The agent then executes the mhove_forwal action for the calculated number of steps.
This entire sequence of primitive actions is then executed by the simulator to complete the high-level action. Algorithm provides formal description of this process.
C.2 DATA COLLECTION AND FILTERING
To generate our dataset, we employ a GenData Agent within the Habitat simulator. At each step, the agent receives an RGB-D observation, which is processed by the APM generate candidate actions. For each action (r, 0), its global landing coordinates are calculated based on the agent's s current pose. An oracle A planner then computes the geodesic distance between each landing point and the goal's global coordinates.
The GenData Agent proceeds by selecting the action with the shortest distance. To collect rich set of diverse yet effective trajectories, we implement a backtracking mechanism: if multiple actions have nearly equal A distances, or if the decision certainty is below a threshold of 0.1, the agent records the current state (pose and candidate actions). After the current episode concludes, the GenData Agent returns to these recorded states to execute alternative choices, thus exploring and recording other viable paths the same goal.
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Algorithm High-level to Low-level Action Translation
1: Input: High-level action Ahigh (r,0)
2: Output: A sequence of low-level actions Stow
3: procedure TRANSLATEACTION Négh))
4: Stow +1
Initialize an empty sequence
5:
D Phase 1: Rotation
6: total Beea__dgrees-te + abs(O x 180/
7: num turns + ceil(total_degreeeeee
8:
if 0 > then
9:
turn lurn_action action <- TURN_LEFT else f < 0 then
10:
11:
aur__action < TURN RIGHT
12:
else
13:
numm tarrssrss
14:
end
for
to reum_furns do Append turn_action to Stow end for
16:
18:
Phase 2: Translation
TrUM forwardowwrrsss + (ril((/0..2))))
20: for i = to num_forwards do
Append MOVE_FORWAAT to Stow
22:
end for
23:
24:
return Stow
25:
end procedure
While this pipeline could theoretically generate a very large dataset, we prioritize data quality and training efficiency, aligning with the "small data, more epochs" characteristic of RFT. We collected an initial set of 54k trajectories. This set was then rigorously filtered. We first used CLIP (Zhao al., 2024) to discard trajectories where the goal image was blurry or semantically unclear (an oc- casional artifact of the simulator). Subsequently, where the agent was stuck in repetitive loops (e.g., constant turning). The final dataset consists of approximately 10k high-quality trajectories, scale consistent with datasets commonly used for GRPO- tasks.
applied rule-based filtering to remove episodes
Challenges in Dense Annotation Generation. This data generation process, although conceptually simple, faces high time costs. The computational cost of performing an A search for every can- didate every timestep across thousands of trajectories is substantial. Our pipeline implements highly parallelized, batched query system to the A planner, making the generation of our large-scale corpus computationally tractable.
D DATASET STRUCTURE AND PROMPTS
D.1 DATASET STRUCTURE
Our training corpus is composed of two distinct sets with differing distributions. The Supervised Fine-Tuning (SFT) set distributed relatively evenly across six categories: chair (N=2,884), toilet (N=2,203), bed (N=1,697), tv screen (N=1,509), sofa (N=1,500), and plant (N=1.300).
In contrast, our Reinforcement Fine-Tuning (RFT) set intentionally curated to reflect the natural long-tail distribution of objects found in the HM3D environment. This results in deliberately im- balanced training set: chair (N=5,697), tv screen (N=2,790), plant (N=1,946), and toilet (N=1,100). This imbalance is not artifact of our design but characteristic of the benchmark itself, which is evident in the official validation set for Instance ImageNav (IIN) that exhibits a similar skew: chair (510), plant (155), bed 13), tv monitor (85), sofa (80), and toilet (57).
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Given this inherent long-tail nature, evaluating similarly skewed test set could fail penalize models that simply overfit to the most frequent categories. Therefore, to provide a more rigorous and unbiased assessment of our model's generalization capabilities, conduct our primary evaluation on the ObjectNav validation set, which features a significantly more balanced distribution: chair (195), sofa (187), toilet (166), bed (165), plant (152), and monitor (135). This allows for stricter test of the agent's 's ability to succeed across both common and rare object categories.
100
240
2700
100
100
Figure 8: Category details of targets in SFT and RFT datasets
D.2 PROMPTS
The following tables show the prompt templates used for the model in various navigation tasks.
Table 9: Prompt for CompassNav in the Object Navigation Task.
SYSTEM PROMPT:
You embodied robotic assistant, with RGB image sensor. You observe the image and instructions. Given to you and output textual response, which is converted into actions that physically move you. Within the environment. You cannot move through closed doors.
USER PROMPT:
TASK: navigate to the nearest (goal.upper()) and get as close to it as possible. Use your prior knowledge about where items typically located within home.
Current observation has (reuMaactios - 1 potential actions shown as red arrows labeled with numbers in white circles. NOTE: choose action if you want turn around dont see any good actions.
Critical Decision Framework:
1. Strategic decision (compose your plan internally, then select action)
Where am I now(pose/heading]? jects/structures/cues for goal. upper()
What do currently see (salient ob-
For each visible action, where will it likely take me next?"
For each visible action, estimate probability of encountering goal.upper(, soon (based on priors, observed cues, spatial continuity, and last-seen evidence)
2. Action Selection
Output the all thinking process in <think></think> and the final answer in <an- swer><<answer>> tags.
Please strictly follow the format. and match potential actions to strategic decision.
3. CONSTRAINTS: Can go and down stairs, but no closed doors.
Table 10: Prompt for Stop Agent in the Object Navigation Task.
SYSTEM PROMPT:
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You are an expert visual analysis agent for a home navigation robot. Your mission is to deter- mine if the robot has successfully reached its target object and should shop.The target object is: uppar.upper)).
USER PROMPT:
The robot should stop ONLY if it is within 1 meter of the target object. This ensures it is close enough for any potential interaction. Additionally, must be directly in front of the CORRECT object with clear, unobstructed view.
CRITICAL INSTRUCTIONS
Distinguish the target from visually similar objects. A CHAIR is not a SOFA, a SOFA is not BED.
Follow the reasoning steps strictly inside <think> tags.
Provide your final command ONLY inside <answer> tags.
Do NOT output anything outside <think> and <answer> tags.
Follow these steps in your reasoning process inside the <think tags:
1. Identify Goal: State the target object you are looking for.
2. Scene Analysis: Briefly describe the main objects you see in the image.
3. Target Verification: the target object igoal.upperty present in the image? Verify its identity against similar objects and state your confidence.
4. Proximity and Distance Assessment: If the target is present, you must evaluate if the robot is within meter of Use visual cues to estimate this distance: Does the object appear very large and fill significant portion of the image? Are fine details, like fabric texture or wood grain, clearly visible? the view completely unobstructed? The robot should only stop if confirmed be within this 1-meter range.
5. Final Conclusion:
Based on all the above, make final decision on whether to stop or continue, and provide brief justification.
Finaly, final Command (inside <answer>): Output ONLY if the robot should STOP. Sutput
ONLY 0 if the robot should CONTINUE.Do NOT output any other characters or words.
EXAMPLE: <think> 1.denniff Goal:The target is SOFA. 2.Scene Analysis: I see living room with a large grey sectional sofa, a coffee table, and TV stand in the distance. 3. Target Verification: The large grey object is definitely SOFA, not armehair confident. 4. 4.Prxiimity and Distance Assessment: The sofa is very large in the frame,The fabric texture is clearly visible. Based these cues, estimate the robot is well within meter of the sofa. The view is unobstructed. This is the correct stopping position. 5.Final Conclusion: The robot has successfully reached the SOFA and is positioned correctly within the 1-meter requirement. It should stop. </think> <answer>1 </answer>
bed. I am highly
Table 11: Prompt for CompassNav in the Instance Image-goal Navigation Task.
SYSTEM PROMPT:
You an embodied robotic assistant, with an RGB image sensor. You observe the image and instructions. Given to you and output textual response, which is converted into actions that physically move you. Within the environment. You cannot move through closed doors."
USER PROMPT:
You can see a composite image composed of two independent images pieced together, separated by white border:
The RIGHT image, labeled 'GOAL IMAGE', shows the TARGET OBJECT (the (goal( "amam)mm))
you need to find.
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The LEFT image represents your CURRENT VIEW (what you are actually seeing now). Current observation has (neunsttions 1) potential actions shown red arrows labeled with numbers in white circles.Note: choose action O you want to turn around or don't ' see any good actions, each arrow represents potential action and is labeled with a number in white circle indicating the location you would move to if you took that action. TASK:
1. Observe the right part. Identify the [goal[ name }]) by focusing on intrinsic attributes like shape, color, and material, ignoring the environment surrounding the object.
2. Use your prior knowledge about where items are typically located within home."
3. Describe what you see and any clues that could help you find the [goal["name]]]
4. Next, decide which direction you should go in.
5. You cannot go through closed doors. You cannot go up or down staircases. Ensure that the object being searched for is on the same floor.
Finally, choose the best action as your final Saaswer.Oullu the thinking process in <think></think> and the final answer <answer></answer> tags.
Table 12: Prompt for Stop Agent in the Instance Image-goal Navigation Task.
SYSTEM PROMPT:
You are an expert visual analysis agent for a home navigation robot. Your mission to determine if your current view precisely matches the goal view, and if you should stop.
USER PROMPT:
You are given a composite image made of two Phrts:The LEFT image is your CURRENT EGO- VhEW.The RIGHT image is the GOAL IMAGE, showing the specific object instance you must find. You should command the robot to STOP only if the CURRENT VIEW contains the exact object from the GOAL IMAGE, and you are in an ideal position for interaction. This means you are directly in front of, and very close to, the target, with a clear, unobstructed view that matches the perspective of the GOAL IMAGE as closely possible. CRITICAL INSTRUCTIONS
The goal is SPECIFIC INSTANCE, not just category. A different chair, even of the same type, is NOT the target.
Pay close attention to fine-grained details: color, shape, texture, and surrounding context to verify the instance match.
Follow the reasoning steps strictly inside <think> tags.Provide your final command ONLY inside <answer> tags.Do NOT output anything outside <think> and <answer>
tags.
Follow these steps in your reasoning process inside the <think tags:
1. Analyze Goal Image (RIGHT): Briefly describe the target object instance in the GOAL IMAGE, noting its key visual features (e.g., 'a wooden armchair with blue cushions') ))
2. Analyze Current View (LEFT); Briefly describe the main objects you see in your CUR- RENT VIEW.
3. Instance Verification: Compare the objects in the CURRENT VIEW to the GOAL IM- AGE, Is the specific target instance present? Justify your conclusion by comparing details like color, model, and wear/tear. State your confidence.
4. Proximity and Distance Assessment: If the target instance is present, evaluate your prox- imity and pose. Does the current view of the object match the scale and perspective of the goal image? Are you positioned for direct interaction?
5. Final Conclusion:
Based on all the above, make final decision on whether to stop or continue, and provide brief justification.
Finaly, final Command (inside <answer>): Output ONLY I if the robot should STOP. ONLY 0 if the robot should CONTINUE.Do NOT output any other characters or words.
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EXAMPLE: <think> L.Analyze Goal Image (RIGHT); The goal image shows a specific wooden dining chair with a distinctive high back and circular, light-colored seat cushion. 2.Analyze Current View (LEFT): see a dining area in my current view. There are several chairs around table. One of them is high-backed wooden chair. I.Instance Verification: Comparing the chair in the current view to the goal image, the wood grain, the high-back design, and the specific circular cushion match perfectly. It is the correct instance. I am highly confident. 4. 4.Proximity and Pose Assessment: The chair in the current view is very close, taking up large portion of the frame, similar to the goal image. I am facing it directly. The pose is ideal for stopping. 5.Final Conclusion: have successfully reached the correct chair instance and am positioned correctly. I should command the robot to stop. </think> <answer>1 </answer>
E
MODEL AND TRAINING DETAILS
E. TRAINING CONFIGURATIONS
Supervised TannnT (SFT) Details We conduct the Supervised Fine-Tuning (SFT) stage us- ing the LLaMA-Factory framework. The training process was performed on A800 GPUs and took approximately 12 hours to complete. The key configurations and hyperparameters are summarized below. We initialize our model from the Owen2. 5 5-VL-7B S-VVL--B-IN 7B-Inst ruct checkpoint. The task is set up as standard supervised fine-tuning problem, with maximum sequence length (cutoff _en) of 4096 tokens.
Fine-tuning Strategy. We employ Parameter-Efficiee Fine-Tuning (PEFT) using the LoRA method to reduce computational overhead. The LoRA modules are applied all projection layers (g_proj, v_proj, k k_proj, o j) as well as the feed-forward layers (gate_pra) j, up up_proj, down j). Key LoRA hyperparameters include a rank (lora_rank) of 64, an alpha (lora_alpha) of 128, and dropout rate of 0.05.
Training and Optimization. The model is trained for 3 epochs with a global batch size of 128 (8 GPUs x 8 ppe__eeviee batch size 2 gradient accumulation setopattionssepss)... We use the AdamW optimizer with a cosine learning rate scheduler, starting with a learning rate of 2 x 10-4 and weight decay of 0.01. The warmup ratio is set to 0.03 of the total training steps. For efficiency, utilize DeepSpeed ZeRO Stage 2, BF16 mixed- -precciion training, and gradient checkpointing.
Reinforcement Fine-Tuning (RFT) Details We conduct the Reinforcement Fine-Tuning (RFT) stage using F a simplified version of the Verl framework. The training was performed on 8 NVIDIA H200 GPUs, with each model being trained for approximately 170 steps. Due to the computational demands of the rollout process, each training step took about 20 minutes. The actor model was initialized from the LoRA weights obtained during the SFT stage. We use the GRPO (adv_es) iimator: grpo) algorithm for policy optimization. A KL penalty (kl_coef) of 1 x 10-2 2 is applied between the actor and the frozen reference policy regularize the policy updates and prevent divergence.
Optimization. We use the AdamW optimizer with a learning rate (1r) of 1 x 10-6 decay of 1 x 10-2 The global batch size for policy updates was set to 128, with a per-device micro-batch size of 4.
6 and a weight
Rollout and Sampling, During the experience generation (rollout) phase, we sample N 5 distinct responses for each prompt from the training set, which is similar to the number of high-level actions generated by APM. The sampling process uses high temperature of 1.0 and top-p of 0.99 to encourage exploration. The rollout batch size set to 512. For evaluation, the sampling more deterministie, with N 1 and temperature of 0.5. Our custom Gap-Aware hybrid reward function, implemented as a Python script, used to score the generated responses.
E.2 JUSTIFICATION FOR A MEMORY-AGNOSTEE TRAINING APPROACH
We acknowledge that sophisticated memory is crucial for embodied navigation. The central debate, however, is not whether to use memory, but how should be integrated with large-scale LVLMs. We identify two distinct integration philosophies:
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First is the the model's input and weights during training. The second is the externally-interfacee memory approach, where complex memory systems (c.g., a E.LAM-generatee topologiaal map) (Yin et al., 2025; Cao et al., 2024) operate as external modules that the agent can query at inference time. We argue that for powerful, pre-trained LVLMs, the latter is more promising and architecturally sound direction. Complex memory systems like SLAM excel at providing structured, metric/topological knowledge, but there is currentl no effective methodology to distill such complex system into the weights of an LVLM via end-to-end training. They are best utilized as supplementary, external tools.
memory approach, where historical context is directly "baked into"
Based on this perspective, we focused our empirical validation on the first philosophy: training- integrated memory. We implemented and tested the two most popular paradigms currently used in the E2E navigation literature for this purpose: summarizing history as text (a-la OctoNav (Gao et al., 2025)) and concatenating historical images (a-la VLN-RI (Qi et al., 2025)). As shown in Table 13, our experiments consistently found that both of these mainstream methods for integrating memory into the training process lead to degradation in performance.
Table 13: Ablation studies on the impact of including historical information in the input prompt. us- ing both the VL--7 and 04-mini models for the Object- Goal Navigation task on HM3Dv2
Model
Method
SR
SPI.
Qwen2.5-VL--7B
No history
45.3
17.5
Qwen2.5-VLL-7B
With history- Text
45.01,0.3
13.5,4.0
Qwen2.5-VL-7B
With history- Image
34.7/10.6
10.946.6
Model
Method
SR
SPL.
04-mini
No history
59.6
26.9
04-mini
With history Text
54.1,5..5
26.3400.6
We attribute this failure to fundamental flaws in how these methods handle historical context. For text summaries, the process of transcribing visual history into language is inherently lossy. It forces rich, high-dimennional visual information through the narrow bottleneck of text, inevitably simplity- ing or omitting crucial details. Furthermore, any errors biases from the VLM performing the tran- scription are then propagated forward, potentially providing the agent with distorted or misleading memory. Conversely, for concatenated images, the challenge becomes one of temporal confusion. We hypothesize the model struggles to consistently distinguish the current, actionable observation from the passive historical frames, sometimes misinterpreting a past view as present reality. This spatiotemporal misalignment can lead to critical errors in oofaloocalzztion and immediate planning.
Therefore, our decision to adopt a memory-agnostae training approach a deliberate and principled one. is not omission, but rejection of the current, ineffective paradigms for wraining-integree memory. By decoupling the core policy from a fixed memory format, we ensure that CompassNav is trained as a pure and robust preision-making core. This modular design makes our framework flexible and future- prooff allowing it to serve as a powerful foundation that can readily integrate with more advanced, exterrally-interffaeed memory solutions as this important research direction evolves.
F REAL-WORLLD DEPLOYMENT CONSIDERATIONS
To validate the practical applicability and sim-to-real transfer capabilities of our CompassNav agent, we successfully deployed on a physical robotic platform. This section details the hardware, system architecture, and qualitative results from our real-world experiments.
Hardware and System Architecture Our experiments were conducted on the ROSMASTER X3(Figure 9). a versatile mobile robot developed by Yahboom. It is equipped with Mecanum wheels that allow for onnidirectional movement, providing high agility in constrained indoor envi- ronments. For perception, the robot utilizes an Orbbec Astra Pro depth camera to capture RGB-D images, which serve as the primary visual input for our model. The robot operates on the ROS! (Robot Operating System) framework, facilitating robust communication and control.
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Figure 9: ROSMASTER X3
Our 7B model is computationally intensive, so our real-world deployment used a three-part system. This setup consisted of the ROSMASTER X3 robot, local computer that acted relay, and a remote server with an NVIDIA H200 GPU to run the CompassNav model. All three devices communicated over the same Local Area Network (LAN). The process worked in continuous loop: the robot sent its current camera view to the local computer, which combined the image with the task instruction (e.g., "find the trash can") and forwarded the pair the server. the server, our model processed the input and decided
action command. This command was sent back to the local computer, which then translated into low-level motor instructions for the robot execute.
This real-time setup allowed our model to act as the robot's 's brain and guide its navigation.
cesss
Fail
Figure 10: Qualitative results from real-world deployment. The first two rows show successful navigation episode controlled by our CompassNav agent, presented from third-person and first- person perspectives, respectively. The agent successfully navigates a complex office environment to reach the "trash can". The third row shows comparative trial using the closed-source model GPT- 4o in a zero-shot setting. Despite its strong general capabilities, GPT-4o fails the task by colliding with obstacle midway through the episode.
Qualitative Sim-to-Real Results We conducted several ObjectNav tasks in real-world indoor of- fiee environments. Figure 10 presents a compelling comparison of navigation episodes for the task "find the trash can".
As illustrated, our CompassNav agent, demonstrates robust navigation capabilities. The first two rows show that it can effectively process real-world visual information, make sensible decisions to maneuver around furniture, and successfully reach its goal. In contrast, the third row highlights the limitations general-purpose purpose model GPT-4o (Hurst et al., 2024) for this embodied task, it strug- gled with spatial reasoning and obstacle avoidance, leading task failure. These results strongly suggest that our proposed training framework not only excels in simulation but also produces a robust policy that successfully transfers to the complexities and visual nuances of the real world, validating the effectiveness of our approach.More detailed deployment videos can be viewed in our supporting materials.
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G QUALITATIVE ANALYSIS
Gon
Thouphe: current shows fixing room area chairs in sight. Ineed find the chol, which ttuble goal image. The goo! image shows dining table with four chairs cheirs). The chalrs in the fiving room are elther couches or en ottonet- not the target. The choira likely off to right side of the living room neor tanle (Deft blue couch. ottomani around living space
(broun
dining crea should first move around couches
TURN ARE
axit balleay right leads oction allow me re-oninnt mavigate
dining mem. only listed
area oction (bum around). Oner/ around locate path the right of the living area where dining room chairs are likely located.
Thought: The gool is to find the choir. In the current viem. see a glass sticing door the outside, closed white door alightly to the left. and blue upholeterad armchair just to the right of my vieapoint. folso see bench is front of and interfor kitchen beyond. The closed un lhe door mould not be digibll. There for formard arrows arrows and read out through the glass door (no chairs outsideg arrow 2 goes the closed door. arrow 5 Eaki me halliey, and arrow 4 goes toward the center of kitchen floor, in order to lnave the fixing room and find the dining toble choose action to move toward and gossibly closer the chol.
Action: 5
Thought am in the bitchen en island On my right ferrew 50 can move into the open diiing area where ane dining chaire arnanged around table The gool to find chair. The choirs cae already see in the right Image look like diving chairs and the left arrow 4 leads down hallway or another room where have yet observed any chairs Therefore the best way fied the chairs vittble the right anage is move indo the dining area ve arrews
Action: 5
Thought: My current slen oppeors in brtchan, (nobing famords contral ballway, The gool find chalr, which located in diming room
span-plan dining
The ovailable
"0: TURN AROUND" and MOVE FORWARD"" Turning (Action 6) mould Wiely orient ma beck into the Altchan, which le lees probuble location for down main corridor which libely connects the linin like
Action
TURN ARO
To find praceeding elong moln thoroughfore most logical exploratory moving advance towards
dining speces
inmediett
Action:
Thought:
hallway with chairs There is
fonwort actions (1 and Lountinees oo doms the hallery tomerde flibely
monrs table dawing
shows diwing
oppear
cushions
shodes.
typically
the soree type of chair. tftwnn around Fretum fikelyy came from. contiruet dlom hallwey tomerd gossible dising bochroom
step.
TURN AROUND
image.
going
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Thought: My current vlew of specious llvnng area, and my abjective norigate to the spectfc cheir Instance shown in the gool image. Fn visualty sconntng the room to find o metch The chalez in the sitting area to the for left exhiiit strong visual pimilarity the terget chair from the cool intos particularly is their design and color Actions through Flead towords the centrol sofo area are different room, which are incorrect destinations. Action previder must direct route the idendjted harget Thengfore, the optimal move is select Action opproach and conforms the motch.
coler
Action:
Thought The cornant vinu shown complete dining including objective novigate
and dining
chains, cleanly visible the side away
Actions
would froyy
entirely Both Actions Land2
correctly
dining
and central poth the table. This
to poution
Thus, Action the best choice.
Action:
Figure 11: A qualitative, step-by-step visualization of our CompassNav agent. Task: find the chair in given image.
25


Reviews:
[{"review_id": "DWmM9qqkuA", "reviewer": "Reviewer_oiRX", "summary": "The paper proposes a vision-language navigation paradigm that shifts from traditional single-path imitation to understanding and modeling the relative value of all candidate actions. The authors built the Compass-Data-22k dataset based on the HM3D environment and designed a gap-aware hybrid reward mechanism that uses a softmax formulation to represent action quality, introducing a dynamic bonus under high-certainty conditions. The model, built upon Qwen2.5-VL-7B, employs a two-stage training strategy of SFT pretraining followed by RFT reinforcement to achieve reasoning-to-action policy learning. Evaluations on the HM3D and MP3D datasets for ObjectNav and ImageGoal tasks.", "questions": "- The paper lacks sufficient implementation details to ensure reproducibility — for example, the exact hyperparameters, model initialization, and architecture adaptation between CLIP and Qwen-7B are not clearly described. Could the authors clarify these details and release training configurations or code references?\n- The proposed two-stage SFT–RFT training involves GRPO optimization and A*-based dense labeling, which appear computationally expensive. Could the authors provide quantitative analysis of training cost (e.g., GPU hours, sample efficiency, or convergence comparison) relative to existing navigation methods?\n- Will the Compass-Data-22k dataset be publicly released? If so, could the authors provide more details about its annotation process and data generation pipeline?\n- How significantly do the number of candidate actions and sampling parameters affect model performance? Have the authors conducted any sensitivity analysis or ablation studies to evaluate this?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- A self-constructed Compass-Data-22k dataset provides dense annotations to support reward modeling.\n- The gap-aware reward dynamically adapts to different uncertainty scenarios, enhancing training stability and exploration capability.", "weaknesses": "- The work mainly combines existing components—standard LVLM architecture and RLHF-style two-stage training—without introducing fundamentally new modeling or optimization techniques. The “gap-aware hybrid reward” is a reasonable design but conceptually close to prior contrastive or entropy-regularized rewards.\n- The two-stage SFT–RFT pipeline and dense A*-based labeling increase training cost and reduce scalability compared to simpler imitation or zero-shot methods.\n- Despite stronger reasoning, CompassNav underperforms recent zero-shot approaches like BeliefMapNav, indicating weaker generalization efficiency.\n[1] BeliefMapNav: 3D Voxel-Based Belief Map for Zero-Shot Object Navigation\n- In the navigation task, the determination of whether the agent has “reached the target” is delegated to an independent large language–vision model (such as Qwen2.5-VL-7B), serving as the Stop Agent. However, large language models are often sensitive to semantic thresholds and contextual ambiguities. As a result, the Stop Agent may prematurely trigger a stop when the target is only partially visible, semantically ambiguous, or occluded, leading to an overestimation of the Success Rate (SR). Therefore, the currently reported SR metric may not accurately reflect the true performance of the navigation system. It is necessary to additionally provide the Stop Agent’s independent accuracy, recall, and SR sensitivity analysis, or to describe the types of misjudgments that occur under occlusion and complex semantic target conditions.\n- The paper proposes a complex framework (including RFT, SFT, reward module, candidate action generation, Stop Agent, and Bonus mechanism), but does not individually verify the necessity of each component. It lacks controlled experiments for key variables such as the number of candidate actions, angular resolution, reward function form, teacher model quality, and sampling strategy. In addition, statistical significance tests could be added to demonstrate the true stability of the performance improvements.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "shRxhvdupG", "reviewer": "Reviewer_6mHY", "summary": "In this work, the authors address the object goal navigation problem. They introduce Compass-Data-22k, a 22k-trajectory dataset comprising 11k reasoning traces with multi-modal reasoning and goal selection for supervised fine-tuning (SFT) and 11k densely annotated reward samples for reinforcement fine-tuning (RFT). Actions are generated through an action proposal module, with rewards computed based on A* geodesic distances. The authors train the CompassNav agent using an SFT–RFT pipeline and propose a gap-aware hybrid reward function that encourages exploration in ambiguous cases and delivers decisive feedback when a clear optimal action exists. The resulting model achieves state-of-the-art performance on goal navigation benchmarks.", "questions": "- Regarding the gap-aware hybrid reward (line 304), what exactly do *best* $d_t^{(1)}$ and *second-best* $d_t^{(2)}$ mean? Are they derived purely from A* distances (i.e., shortest path to goal)? Or based on the model’s predicted actions (best corresponds to the most likely action output) and their corresponding A* distances?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The authors commit to open-sourcing both the dataset and codebase, which enhances reproducibility and can meaningfully benefit the research community.\n\n- The proposed Compass-Data-22k dataset is well-structured and provides dense annotations that can facilitate broader research in embodied navigation and decision modeling.\n\n- The gap-aware hybrid reward function is an interesting contribution, as it attempts to balance decisiveness and exploration through adaptive reward modulation.\n\n- The inclusion of real-world robot deployment results complements the simulation benchmarks and strengthens the practical relevance of the work.", "weaknesses": "- Although the authors claim state-of-the-art performance, the improvements over baselines are marginal and may not clearly justify the methodological complexity.\n\n- The claim that the model achieves “decision understanding” and this paper introduces “a new paradigm” seems to be overstated. \n    - The reasoning traces used for SFT are synthetically generated by another multimodal LLM and may not reflect causal or grounded reasoning. Furthermore, since no contextual history is preserved between steps, the reasoning is effectively discarded in subsequent decisions. For instance, in Figure 2(3), the generated rationale—“I want to go inside the kitchen…”—would be forgotten by the next invocation, leaving the robot unaware of why it entered the kitchen in the first place. As a result, these generated explanations appear arbitrary and disconnected from actual decision-making, contributing little to “decision understanding.”\n    - During RFT, the reasoning outputs are not incorporated into the reward computation, suggesting that the model’s reasoning is superficial rather than functionally integrated. As such, the method does not have much difference from standard robot learning paradigms (e.g., pretraining and finetuning using reinforcement learning with dense rewards).\n\n- The motivation and necessity for the use of LVLMs are not fully convincing. Given access to depth and semantic information, classical navigation pipelines (e.g., SLAM + frontier exploration + object recognition) could potentially solve the same problem efficiently without large-scale data or training. Why use LVLMs for such tasks?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5FTGk4o1VT", "reviewer": "Reviewer_Bopw", "summary": "This paper proposes CompassNav, a paradigm for training large vision-language models (LVLMs) in navigation that shifts from Path Imitation to Decision Understanding. Instead of merely replicating expert trajectories, the authors aim to build agents that reason about the relative quality of alternative actions. To achieve this, the authors introduce compass-data-22k, a large-scale dataset that annotates every feasible action using A* geodesic distances, and design a gap-aware hybrid reward that dynamically adjusts feedback according to decision certainty. The training pipeline consists of  supervised fine-tuning stage and a reinforcement fine-tuning stage. Experiments on standard navigation benchmarks (HM3D, MP3D) show that CompassNav outperforms modular and end-to-end baselines, achieving new state-of-the-art results in object  and image-goal navigation.", "questions": "What is the inference speed of this approach?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper articulates a clear paradigm shift from trajectory imitation to decision-space reasoning. This reframing is conceptually strong.\n\nThe two-stage SFT-RFT training pipeline is methodically designed and empirically justified, overcoming the cold-start issue commonly faced in reinforcement fine-tuning.", "weaknesses": "While the paper claims to enable 'decision understanding',  the experiments still rely on conventional navigation metrics (SR, SPL). There is no explicit evaluation of whether the model actually understands or reasons about its decisions.  Some experiments similar to basic instruction-following tests could help the paper illustrate this issue, such as the tests conducted in [1]. Meanwhile, [1] involves VLM-based indoor navigation. Related comparisons or discussions are strongly recommended.\n\n\n[1] Wang, Z., Wu, M., Cao, Y., Ma, Y., Chen, M., & Tuytelaars, T. (2024). Navigating the nuances: A fine-grained evaluation of vision-language navigation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EL4hijC0z8", "reviewer": "Reviewer_iCB1", "summary": "This paper proposes CompassNav, a two-stage training framework that shifts the paradigm of embodied navigation from path imitation to decision understanding.\nThe authors argue that traditional imitation learning constrains embodied agents to replicate single expert trajectories, thereby limiting their exploration and generalization capability.\nTo address this, the paper introduces a large-scale dataset, Compass-Data-22k, which provides panoramic supervision signals by annotating all possible actions with A* geodesic distances. In addition, a gap-aware hybrid reward function is designed to modulate feedback strength based on decision certainty, which is then further integrated within a GRPO framework.\nThe resulting pipeline enables the 7B-parameter CompassNav model to develop an internal “compass,” allowing it to evaluate action quality beyond simple imitation.\nExperimental results show that CompassNav achieves state-of-the-art performance on HM3Dv1/v2 and MP3D benchmarks and generalizes robustly to real-robot scenarios.", "questions": "The authors should explain more about the “decision understanding”. Are there any ways/metrics that help to demonstrate CompassNav’s reasoning rather than imitation bias?\n\nIn GRPO, the KL regularization term constrains policy updates relative to the SFT reference model. Do the authors experiment with adaptive or decayed KL coefficients to allow more flexible exploration during late training?\n\nThe RFT subset selectively explores “ambiguous nodes.” How is the sample balance ensured to prevent overrepresentation of uncertain scenarios in training?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper makes a clear conceptual shift from imitation to understanding, reformulating navigation as a decision-making problem rather than a trajectory replication. This shift is appealing, helping to address the fundamental bottleneck of imitation-based LVLM training.\nThe Compass-Data-RFT-11k subset provides per-allocated A* annotations, offering richer training signals than existing single-path datasets. The data collection pipeline is transparent, reproducible, and logically linked to the proposed reward mechanism.\nThe gap-aware hybrid reward integrates the notions of distance-based preference and decision certainty in a simple but effective way. It adapts to ambiguous decision spaces and contributes to stable reinforcement fine-tuning.\nThe CompassNav agent consistently outperforms both modular pipelines (e.g., CogNav, UniGoal) and end-to-end LVLMs (e.g., GPT-4o, Nav-R1) despite using significantly fewer training samples. Results on real-world robotic navigation further support the performance.", "weaknesses": "*Conceptual ambiguity in “Decision Understanding”*\n\nThe paper repeatedly emphasizes “understanding” but does not quantitatively measure what constitutes understanding beyond improved reward signals. It remains unclear whether CompassNav truly learns transferable decision reasoning or merely fits dense reward distributions.\n\n*Reliance on A\\* as oracle*\n\nWhile efficient, the A*-based distance labeling assumes deterministic environments and may not reflect realistic perception noise or dynamic obstacles. This could introduce bias into both reward computation and policy evaluation, especially in more practical environments.\n\n*Limited diversity of evaluation environments*\n\nExperiments focus mainly on HM3D variants and MP3D, which are visually and structurally similar. Broader generalization tests (e.g., Gibson, AI2-THOR, or out-of-domain tasks) would strengthen claims of decision-level robustness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)