Title: Aux-Think: Exploring Reasoning Strategies for Data-Efficient Vision-Language Navigation
Venue: NeurIPS 2025
Accepted: yes
Keywords: vision-language navigation

chain of thought

data efficiency
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nI 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.\n\nLIMITATIONS:\nThe authors provide a discussion of the method's limitations, which I think reasonable and fair.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-23"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths

1 The whole paper is well strcutred and layout is clear

Weakness  

1 A dataset is listed as contribution but the introduction details and stats about the dataset is not enough.   

2 The experimental results may not be stastically significant.

QUESTIONS:
1 how are the retrospective thinking data labelled?  Do these data contain self-correct information?

2 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.  

3  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
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-28

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n* **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.\n\nQUESTIONS:\nThe 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.\n\nLIMITATIONS:\nNo.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nPlease see my comments on weaknesses above.\n\nLIMITATIONS:\nThe paper does have limitation section.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}