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Probing Confidence Regions for Early Exits in Chain-of-Thought |
Soundness: 2: fair
Presentation: 3: good
Contribution: 2: fair
Rating: 2: reject
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
This paper proposes CORE, a training-free early-exit framework for CoT reasoning in large language models. The key idea is to monitor the entropy dynamics of intermediate reasoning steps, observing that CoT generation typically transitions from a high-entropy uncertainty region to a low-entropy confidence region. CORE detects this transition using a sliding-window average of entropy and halts generation once the model stably enters the low-entropy region, aiming to reduce redundant reasoning without hurting accuracy.
1. Clear empirical observation of entropy dynamics. The paper provides a clean and consistent empirical finding that CoT reasoning trajectories often exhibit a transition from high-entropy exploration to low-entropy convergence, which is intuitively interpretable and empirically supported by multiple examples.
2. Simple yet effective method. The proposed CORE mechanism is lightweight, training-free, and easy to implement across different LLMs. It offers a practical solution for improving reasoning efficiency without requiring additional model fine-tuning.
1. Heuristic nature of the method. Despite the appealing intuition, the proposed CORE remains largely a heuristic extension of existing confidence-based early-exit methods. The confidence region is defined purely through an empirical entropy threshold and sliding window, without theoretical grounding or formal justification that low entropy reliably corresponds to reasoning completeness.
2. The entropy threshold (β) and window size (w) are tuned empirically on a specific dataset (Bespoke-Stratos-17k) and directly applied to test tasks. This raises concerns about generalization and stability across domains or reasoning styles.
3. The main empirical observation that CoT entropy decreases as reasoning progresses is not entirely new. Similar dynamics have been reported in recent works [1,2]. The proposed framework essentially repackages this known pattern rather than providing new methodological or conceptual insights. The proposed confidence region essentially monitors whether the token-level entropy remains below a threshold β for w consecutive steps. In other words, it merely adds a smoothing window on top of standard confidence-based early-exit heuristics
4. The baselines (DEER, Dynasor) are relatively early heuristic methods. The paper does not compare against recent adaptive or RL-based approaches such as ThinkPrune[1], SelfBudgeter[2], which limits the strength of the empirical claims.
[1] Hou, B., Zhang, Y., Ji, J., Liu, Y., Qian, K., Andreas, J., & Chang, S. (2025). Thinkprune: Pruning long chain-of-thought of llms via reinforcement learning. arXiv preprint arXiv:2504.01296.
[2]Li, Z., Dong, Q., Ma, J., Zhang, D., Jia, K., & Sui, Z. (2025). Selfbudgeter: Adaptive token allocation for efficient llm reasoning. arXiv preprint arXiv:2505.11274.
See weaknesses |
Fully AI-generated |
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Probing Confidence Regions for Early Exits in Chain-of-Thought |
Soundness: 3: good
Presentation: 3: good
Contribution: 2: fair
Rating: 4: marginally below the acceptance threshold
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
This paper introduces CORE (COnfidence Region Exit), a training-free method to improve the efficiency of Chain-of-Thought (CoT) reasoning in large language models. The authors observe that a model’s reasoning entropy follows a consistent two-phase pattern: an initial high-entropy “uncertainty region” where answers fluctuate and are unreliable, followed by a low-entropy “confidence region” where reasoning stabilizes and accuracy plateaus. Building on this finding, CORE detects entry into the confidence region using a sliding-window entropy threshold and halts generation once stability persists, thus avoiding both premature exits and redundant post-solution verbosity. The experiments show that CORE has better effectiveness and efficiency trade-off compared with the baselines.
1. Present a clear empirical observation of reasoning dynamics, especially the two-phase entropy pattern, quite interesting.
2. The method itself is simple and training-free.
3. Experiments on diverse tasks and models shows the generality of the method.
4. Easy to follow.
1. It seems the proposed method relies on a hidden assumption: the model is calibrated well. If the model is poorly calibrated, the model can be easily trapped in a low-entropy region but with wrong answers. This paper does not have sufficient discussion on this.
2. This work seems to only discuss the short CoT cases, i.e., only one reasoning chain is generated. Reasoning models like Deepseek-R1 or other models that involve test-time scaling, can generate multiple CoT chains separated by tokens like "wait". In this case, there may exist a transition from the low-confidence region into the high-entropy region back and forth. In this case, how to decide when to exit using the confidence region still needs consideration.
3. While the proposed method can improve the effectiveness, it is a simple extension of identifying the low entropy point into identifying a low entropy period. The proposed method seems to be incremental. Besides, the thresholding method (for both thresholds and window size) lacks sufficient discussion. The current hyperparameters are determined on an auxiliary dataset, and perhaps a better or more principal tuning method could be discussed.
See weakness. |
Fully human-written |
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Probing Confidence Regions for Early Exits in Chain-of-Thought |
Soundness: 2: fair
Presentation: 3: good
Contribution: 2: fair
Rating: 4: marginally below the acceptance threshold
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
This paper addresses the inefficiency of Chain-of-Thought (CoT) reasoning in LLMs. The authors identify a core dynamic: CoT processes consistently transition from a high-entropy "Uncertainty Region" to a stable, low-entropy "Confidence Region," with the latter reliably signaling reasoning completion. Based on this insight, they propose CORE (Confidence Region Exits), a simple, training-free early exit method that uses a sliding window to detect this transition and terminate generation. Extensive experiments show that CORE achieves a superior accuracy-efficiency trade-off compared to existing early exit strategies.
1. The proposed CORE method is elegant, training-free, and model-agnostic.
2. The claims are backed by rigorous and comprehensive experiments across multiple models and challenging benchmarks.
W1: Insufficient Analysis of Hyperparameter Sensitivity: The performance of CORE hinges on two key hyperparameters: the entropy threshold β and the window size w. The paper lacks a thorough discussion and sensitivity analysis of their selection, which leaves open questions about the method's robustness and ease of use in broader scenarios.
W2: Limited Scope of Task Domains in Evaluation: The experiments are exclusively focused on analytical reasoning tasks (math, QA). The core assumption that CoT reasoning converges to a stable low-entropy state may not hold for more open-ended, generative tasks, narrowing the demonstrated applicability of the CORE method.
W3: Under-discussed Overhead of the Probing Mechanism: The paper does not sufficiently quantify the computational overhead introduced by the "probing" step itself. A more detailed analysis of this trade-off is needed, especially for simpler problems with short reasoning chains.
W4: Failure to Distinguish Between Different Failure Modes: The paper does not analyze how CORE behaves under different types of reasoning failures, such as "confident failures" (converging to a low-entropy wrong answer) versus "lost failures" (never reaching a confidence region). This analysis is crucial for a complete understanding of the method's limitations and risk profile.
Q1 (Regarding W1): Could you please provide a sensitivity analysis or ablation study for the hyperparameters β and w to help understand the method's robustness?
Q2 (Regarding W2): To what extent do you believe the core assumption of a two-phase entropy dynamic generalizes to more creative or generative CoT tasks?
Q3 (Regarding W3): Have you considered more cost-effective probing strategies, such as adaptive or sparse probing, to minimize the latency overhead?
Q4 (Regarding W4): Could you provide a breakdown of the failure cases in your experiments (e.g., "confident failures" vs. "lost failures")? This would offer a clearer picture of the method's failure modes. |
Fully AI-generated |
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Probing Confidence Regions for Early Exits in Chain-of-Thought |
Soundness: 2: fair
Presentation: 3: good
Contribution: 2: fair
Rating: 4: marginally below the acceptance threshold
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
This paper addresses the efficiency problem of CoT reasoning in LLMs. The authors point out that while CoT enhances performance, it also leads to high computational costs and latency due to redundant token generation. The paper's main contributions are twofold: First, the authors claim to be the first to empirically reveal a global dynamic pattern of entropy during CoT reasoning. They find that the reasoning process transitions from a high-entropy Uncertainty Region to a low-entropy Confidence Region, arguing that entering the latter is a strong signal of reasoning completion. Second, based on this insight, the authors propose Confidence Region Exits (CORE), a lightweight, training-free algorithm to detect if the model has stably entered the Confidence Region, thereby triggering an early exit. Experiments show that CORE achieves a superior Pareto trade-off between token consumption and accuracy compared to baselines like DEER and Dynasor.
- Significance: The paper addresses an very important and widespread problem: the computational efficiency of CoT reasoning. Improving efficiency is critical for deploying powerful LLMs in resource-constrained environments and for reducing the cost and latency of large-scale inference, making it highly significant to the LLM community.
- Originality: The paper's strongest contribution is its pioneering empirical investigation to reveal the underlying two-phase entropy dynamics of CoT reasoning. This novel model of partitioning the process into an Uncertainty Region and a Confidence Region provides the field with a new perspective for understanding LLM internal states and reasoning redundancy.
- Quality: The experimental design, on the metric of token-accuracy trade-off, is solid. The results show that CORE achieves a Pareto-optimal trade-off over existing early exit methods on multiple models and benchmarks.
- Clarity: The paper is well-written and easy to follow. Figure 1a, in particular, is an exceptionally clear and powerful visualization that effectively communicates the authors' core insight. Figure 2 also clearly and concisely explains the CORE algorithm's mechanism.
1. The proposed CORE method relies on static hyperparameters ($\beta$ and $w$), which is its most significant flaw. The authors' own data proves this approach is brittle:
- Model-Sensitive: Table 4 shows a 10x difference in $\beta$ (0.2 vs 0.02) across models, indicating a lack of portability and requiring expensive, model-specific tuning.
- Task-Sensitive: Figure 4 shows it fails to adapt to task difficulty within the same model. On hard problems for Qwen, token savings lead to a proportional accuracy drop, a very poor trade-off, proving the threshold prematurely cuts off necessary thinking.
2. The algorithmic innovation of CORE itself is minimal. It is an incremental engineering improvement over existing confidence-based methods like DEER. DEER (single-step entropy $\le \beta$) is just a special case of CORE with $w=1$. Adding a sliding window ($w$) is a standard signal-smoothing technique (to avoid the glitch in Figure 1b, left) and does not constitute a major conceptual innovation.
3. The claim about efficiency in this paper is not rigorous. The delay analysis in Section 4.4 is misleading. To demonstrate the performance of CORE, the author should also provide a latency comparison among CORE, DEER, and Dynasor, as all three introduce an extra probing overhead that is currently unaccounted for.
1. The paper's core claim is to improve efficiency, which implies reducing not just token cost but also practical latency. Your analysis in Section 4.4 compares CORE to Vanilla to claim that probing overhead is minimal. This comparison is considered to be invalid as it conflates the gains from exiting early with the costs introduced by probing. To fairly assess efficiency advantage of CORE, could you please provide a latency comparison between CORE, DEER, and Dynasor on the same configuration?
2. Table 4 reveals a 10x order-of-magnitude difference in the $\beta$ threshold between DeepSeek-R1-Distill-Qwen-7B (0.2) and Qwen3-14B (0.02). This seems to contradict the paper's claim of seamless adaptability. Can you explain the reason for this drastic variance? If every new model requires an expensive tuning on a large dataset (Bespoke-Stratos-17k training split in your setting), how can the method be considered lightweight or seamless-adapted?
3. Figure 4 shows that on hard problems for Qwen3-14B, token savings lead to a proportional accuracy drop. This is a very poor trade-off and strongly suggests CORE cannot simultaneously adapt to both easy and hard tasks. Does this imply that one of your core contributions (dynamics of entropy and accuracy across reasoning stage) fails when strong models tackle hard problems?
4. We acknowledge your discovery of the two-phase entropy pattern is novel. But we consider CORE itself an engineering improvement (using a standard signal smoothing technique) specifically designed to solve the failure mode of DEER to premature exits on single-step entropy glitches (as shown in Figure 1b, left). How do you explain your contribution?5.The paper mentions in Section 2 that $T_i$ is a "sequence of tokens (e.g, fixed interval of tokens)." Please clarify the value of this fixed interval used in your experiments and your justification for choosing it. |
Fully human-written |