How to Mitigate Overfitting in Weak-to-strong Generalization?
Authors: Junhao Shi, Qingyuan Chen, Zhaoye Fei, Yining Zheng, Qipeng Guo, Xipeng Qiu
Accepted at: acl25

How to Mitigate Overfitting in Weak-to-strong Generalization?

Junhao Shi1,2*, Qinyuan Cheng1*, Zhaoye Fei1,2, Yining Zheng1,
Qipeng Guo2,3†, Xipeng Qiu1,2‡
1Fudan University 2Shanghai Innovation Institute 3Shanghai AI Laboratory
{jhshi24, chengqy21}@fudan.edu.cn,
guoqipeng@pjlab.org.cn, xpqiu@fudan.edu.cn

Abstract

Aligning powerful AI models on tasks that surpass human evaluation capabilities is the central problem of superalignment. To address this problem, weak-to-strong generalization aims to elicit the capabilities of strong models through weak supervisors and ensure that the behavior of strong models aligns with the intentions of weak supervisors without unsafe behaviors such as deception. Although weak-to-strong generalization exhibiting certain generalization capabilities, strong models exhibit significant overfitting in weak-to-strong generalization: Due to the strong fit ability of strong models, erroneous labels from weak supervisors may lead to overfitting in strong models. In addition, simply filtering out incorrect labels may lead to a degeneration in question quality, resulting in a weak generalization ability of strong models on hard questions. To mitigate overfitting in weak-to-strong generalization, we propose a two-stage framework that simultaneously improves the quality of supervision signals and the quality of input questions. Experimental results in three series of large language models and two mathematical benchmarks demonstrate that our framework significantly improves PGR compared to naive weak-to-strong generalization, even achieving up to 100% PGR on some models.

1 Introduction

Large language models (LLMs) have progressed rapidly in recent years, achieving superhuman ability in diverse tasks, and showing great potential in pursuing superhuman intelligence. Although large language models acquire extensive world knowledge and excellent capabilities to complete complex tasks through large-scale pre-training, alignment is still necessary to ensure that these models carry out tasks according to human intentions

![Illustration of different weak-to-strong generalization approaches. (a) Conventional approach with noisy labels from weak model, indicated by black dots; (b) Simple filtering approach that discards too many valuable hard samples; (c) Our framework can maintain both supervision quality and question quality.](page_768_355_496_312.png)

Figure 1: Illustration of different weak-to-strong generalization approaches. (a) Conventional approach with noisy labels from weak model, indicated by black dots; (b) Simple filtering approach that discards too many valuable hard samples; (c) Our framework can maintain both supervision quality and question quality.

(Ouyang et al., 2022). The hard problem of alignment is “How do we align systems on tasks that are difficult for humans to evaluate? (Leike, 2022)” This challenge is known as superalignment, which refers to how humans can align models on tasks that are beyond human ability to evaluate, which means that humans cannot provide correct supervision. One notable method in superalignment is the weak-to-strong generalization (Burns et al., 2023): How can weak supervisors supervise stronger models? This concept describes how the capacity of strong students can be elicited by fine-tuning on data labeled by weak teachers, consistently enabling them to outperform their weak teachers. In specific experiments, a weak model is typically used as a weak teacher, while a more capable model serves as the strong student.

Figure 1(a) demonstrates the features of weak-to-strong generalization, labels generated by the weak model contain noise due to its limited capabilities,
Figure 2: Overview of our two-stage training framework. **Stage I (top):** The raw question set is filtered based on weak model’s consistency (\( \mathbb{S} \)). High-consistency questions are used to generate Training Set A, which is then used for finetuning the strong model (\( \mathbb{S} \)). **Stage II (bottom):** Previously discarded questions are re-evaluated and refined using the finetuned strong model from Stage I (\( \mathbb{S} \)). High-consistency questions are selected to form Training Set B, which is then combined with Set A for final finetuning (\( \mathbb{S} \)). Here \( \mathbb{S} \) represents weak model, \( \mathbb{S} \) represents primary strong model, \( \mathbb{S} \) represents Stage I finetuned model, and \( \mathbb{S} \) represents final finetuned model.

thus presenting lower correctness and adding difficulties in eliciting strong model’s capabilities. As a result, the strong model may overfit the erroneous weak supervisions, leading to performance degeneration (Yang et al., 2024a). Recent research has introduced filtering techniques to improve label correctness (Guo and Yang, 2024), making the analogy similar to easy-to-hard learning (Hase et al., 2024). In contrast to these related studies, we conduct a more in-depth investigation into the effects of commonly used data filtering methods. Based on our experimental results, we highlight that an excessive emphasis on data filtering can lead to data degeneration since some hard samples can be discarded, which may hinder the overall performance, as shown in Figure 1(b). In contrast, Figure 1(c) illustrates an ideal scenario, where a clean training set, containing both strong and weak samples, facilitates improved generalization. **These hard samples may be important to elicit student’s capabilities to solve hard problems.**

According to the expansion theory proposed by Lang et al. (2024), weak-to-strong generalization emerges through two fundamental mechanisms: pseudolabel correction and coverage expansion, where models learn to rectify teacher’s errors while extending to areas of teacher uncertainty. While conventional approaches like filtering effectively enhance pseudolabel correction by improving supervision quality, this improvement often comes at the expense of reduced question quality, particularly in terms of difficulty distribution and diversity. This trade-off can significantly impair coverage expansion, thereby compromising the overall generalization capability.

Therefore, to mitigate overfitting and improve weak-to-strong generalization, we propose a two-stage weak-to-strong training framework, as depicted in Figure 2. In the first stage, we enhance supervision quality by filtering the generated samples based on weak model’s uncertainty, which is estimated through the model’s self-consistency. In the second stage, we further augment question quality by reusing the discarded questions and leverage the previous finetuned strong model to generate answers, as finetuned strong model may solve difficult questions better, incorporating those with high confidence back into the training dataset, to further elicit strong model’s capabilities.

We assess the effectiveness of our framework on two popular mathematical reasoning benchmarks: GSM8k (Cobbe et al., 2021) and MATH (Hendrycks et al., 2021). The evaluation involves two distinct model series: Llama 3 (Dubey et al., 2024) and Deepseek (Bi et al., 2024). The results demonstrate the substantial improvements offered by our framework. Specifically, the first stage outperforms the standard weak-to-strong method,
while the second stage further enhances data quality and narrows the performance gap. On the commonly used criteria *performance gap recovered (PGR)*, our framework significantly outperforms conventional weak-to-strong finetuning, reaching or surpassing 100% on certain models and datasets.

The main contributions of this paper are concluded as follows:

1. We pinpoint two critical factors for mitigating overfitting in weak-to-strong generalization: the quality of supervision and the quality of questions. And we demonstrate that enhancing supervision quality through data filtering leads to degeneration in question quality, which may harm the model’s generalization on hard questions.

2. We introduce a two-stage weak-to-strong training framework focusing on supervision quality and question quality, effectively address overfitting on challenging reasoning tasks.

3. We conduct extensive experiments on MATH and GSM8k using model series including Llama 3 and Deepseek. The results demonstrate that our framework effectively mitigates overfitting, in which our first stage significantly outperforms the conventional weak-to-strong generalization method, and the second stage further enhances PGR with notable robustness, providing strong evidence of the effectiveness of our framework.

2 Background

In weak-to-strong generalization, the primary focus is how to elicit the ability of superhuman models using supervision from humans, as there is no access to superhuman tasks and superhuman models. The terms *Weak* and *Strong* here refer to model’s latent potential, indicating human and superhuman models in the superalignment hypothesis.

Generally, the weak-to-strong generalization process involves the following steps, originally proposed by Burns et al. (2023):

1. Creating a weak supervisor: The weak supervisor referred to as *Weak Model*, is typically made by training small pretrained models. Its performance is referred to as *weak performance*.

2. Training strong models with weak labels: Data labelled by the weak model is used to finetune a large pretrained model, with the resulting performance termed *weak-to-strong performance*.

3. Training the strong ceiling: Ground truth data, used in the second step, is employed to fine-tune the large pretrained model, resulting in *strong ceiling performance*.

In the context of weak-to-strong generalization, the Performance Gap Recovered is a commonly adopted criterion, introduced by Burns et al. (2023), to assess how effectively the potential of the strong model is elicited. A higher PGR indicates improved weak-to-strong performance, as it reflects the ability of the finetuned strong model to achieve performance closer to the "strong ceiling," thereby demonstrating the effective extraction of the model’s full potential. The PGR is mathematically defined as:

\[
PGR = \frac{\text{weak-to-strong} - \text{weak}}{\text{strong ceiling} - \text{weak}}.
\]

In a specific model series, models’ weak or strong can be directly represented by their model size, as a weak instruct model may outperform its strong under-elicited pretrained model, but still underperforms the strong finetuned model (e.g., Llama 3 8B Instruct vs Llama 3 70B & Llama 3 70B Instruct). In this work, we simplify weak supervisor’s training by selecting the instruct versions of the current state-of-the-art models, as they show more human-like behaviours and generate more natural answers.

3 Methodology

An overview of our framework is illustrated in Figure 2. In the first stage, we use an uncertainty-based criterion to filter data labelled by the weak model, samples are filtered based on model’s consistency and are then used to train the strong model. In the second stage, we reuse discarded questions showing high uncertainty for weak model in Stage I by employing the finetuned strong model to provide supervision. To ensure the correctness of the supervisions in Stage II, we also employ an uncertainty-based filtering criterion to retain the more accurate supervisory signals. Our framework simultaneously improves both the quality of supervision and the quality of questions in the weak-to-strong
process, enhancing the generalization ability of weak-to-strong training.

3.1 Stage I: Purifying Supervision Signals
With given weak model \( M_{weak} \), strong model \( M_{strong} \) and a set of questions, conventional weak-to-strong generalization directly use weak model to generate answers, then use generated samples to train strong model. However, due to weak model’s limited ability, generated labels may contain many noisy labels showing low supervision quality, causing overfitting during strong model finetune. To purify noisy supervision, we introduce an uncertainty-based filter, choosing samples with high model consistency. We employ chain-of-thought prompting to randomly generate ten responses for each question, thereby ensuring a diverse set of possible answers. Among these, we select the answer with the highest consistency as the model’s final response, as it reflects the greatest confidence in the reasoning process. Specifically, for a selected answer Ans, which appears \( N_{Ans} \) times out of a total of \( N_{Total} \) samplings, the model’s confidence in that answer is defined as:

\[
\text{Confidence(Ans)} = \frac{N_{Ans}}{N_{Total}} \times 100\%.
\]

To filter out noisy labels and improve supervision quality, we apply an uncertainty-based filter based on model’s confidence. By filtering samples with a consistency threshold, we form a filtered dataset of high-confidence question-answer pairs, shown as "Training set A" in Figure 2, showing higher supervision quality. Our experiments show that with higher consistency threshold results in higher sample correctness, as shown in Figure 3. We finally use the filtered dataset to finetune strong model, expecting to solve the problem of overfitting on wrong labels.

![The relationship between supervision correctness and filtering threshold. As the filtering threshold increases, the supervision correctness (measured by label accuracy) shows a consistent upward trend.](page_186_154_377_246.png)

Figure 3: The relationship between supervision correctness and filtering threshold. As the filtering threshold increases, the supervision correctness (measured by label accuracy) shows a consistent upward trend.

We further analyzed the effectiveness of chain-of-thought prompting, detailed in Appendix 5.4.

3.2 Stage II: Mitigating Question Degeneration
Following Stage I, the finetuned model \( M_{finetune} \) and two distinct datasets are produced: a filtered dataset \( D_{filtered} \) containing high-certainty questions and a discarded dataset \( D_{discarded} \) comprising low-certainty questions. The discarded questions often represent questions with higher difficulty or less common topics, where the weak model struggled to provide confident answers. Despite this, these questions remain crucial for improving overall model performance, as the test set typically encompasses a diverse range of difficulty levels and topics. Meanwhile, the finetuned model in Stage I, having its ability elicited by labels from weak teacher, now outperforms its weak teacher, showing the potential to solve questions beyond weak model’s ability.

To address this, the finetuned student model—now exceeding the weak model in performance—is employed to generate answers for the discarded questions. For each question in the discarded question set, the finetuned model generates a variety of potential answers, providing a more accurate and comprehensive set of responses than its teacher. Similar to Stage I, an uncertainty-based filtering process is applied to retain only high-confidence samples, producing a high quality dataset, shown as "Training set B" in Figure 2.

The refined, high-certainty samples are then appended to the training set, creating an enriched dataset. This updated training set is subsequently used to finetune the initial strong model, enhancing its ability to generalize across the full spectrum of question difficulty and diversity. This refinement process ensures the inclusion of valuable but initially uncertain data, maximizing the strong model’s potential and overall performance.

4 Experiments

4.1 Experimental Settings
Dataset  We conduct experiments on two prominent mathematical reasoning benchmarks, the grade-school level reasoning task GSM8K (Cobbe et al., 2021) and the more challenging MATH task (Hendrycks et al., 2021). For training, we use the same training set as Yang et al. (2024b) for both weak model labelling and strong model training.
Figure 4: (a) The upper row shows the performance trajectory and PGR across different stages (Baseline, Stage I, and Stage II). The solid lines represent model performance (left y-axis), while the dash-dotted lines show PGR values (right y-axis). (b) The lower row demonstrates the impact of different filtering thresholds on model performance, with triangles representing Stage I results and circles representing Stage II results. For each experimental setting, points with the same color correspond to the same Stage I filtering threshold. Results show consistent improvement patterns across all model configurations, with Stage II generally achieving better performance than Stage I.

For evaluation, we utilized the GSM8K evaluation set, which contains 1,319 data points. For MATH, we used the smaller subset as the primary evaluation test set following Lightman et al. (2024), which contains 500 data points. We compared the model’s performance on the 500 samples subset with that on the original test dataset, with details provided in Appendix 5.5.

Models We use several models to investigate the effectiveness of our framework, including the Llama 3 series (Dubey et al., 2024) (Llama 3 8B Instruct, Llama 3 70B) and the Deepseek series (Bi et al., 2024) (Deepseek 7B Chat, Deepseek 67B Base).

Evaluation Metrics We use accuracy and performance gap recovered (PGR) as our primary evaluation metrics. For PGR, we define the performance of small instruct/chat models as "weak performance", and the performance of strong models after finetuned with golden labels as "strong ceiling", each representing the starting and the goal performance we aim to achieve. Both metrics were employed to assess the effectiveness of the weak-to-strong generalization approach, highlighting the elicited abilities of the model and the extent to which the performance gap was recovered.

4.2 Main results

As illustrated in Figure 4, our framework significantly narrows the performance gap between fine-tuned strong model and strong ceiling, meanwhile effectively eliciting strong model’s ability. Our experimental results demonstrate the efficacy of our framework across multiple model series, including Llama 3 and Deepseek. For the Llama 3 model, specifically the 70B variant, the performance in weak-to-strong generalization (PGR) on the GSM8K dataset shows a remarkable improvement, rising from 7.19% to 120.50% when utilizing the smaller Llama 3 8B Instruct model as the weak model. This improvement is accompanied by an increase in task performance, which climbs from 75.20% to 81.50%. Similar enhancements are observed on the MATH dataset, where PGR increases from 36.17% to 121.28% and task performance rises from 18.2% to 35.2%.

Comparable gains are seen with the Deepseek model series. On the GSM8K dataset, PGR increases significantly from 51.39% to 90.04%, while task performance improves from 62.39% to 72.94%. For the MATH dataset, PGR improves from 65.85% to 126.83%, with performance rising from 16.8% to 21.8%.
4.3 Performance Gains from Enhanced Supervision Quality

As illustrated in Figure 4(a), the uncertainty-based filtering approach implemented in Stage I consistently outperforms the conventional baseline across multiple datasets and model configurations. Specifically, for Llama 3 on the GSM8K dataset, the weak-to-strong generalization performance improves substantially from 7.19% to 98.56% in PGR, accompanied by an increase in task performance from 75.20% to 80.28%. On the MATH dataset, PGR rises from 36.17% to 112.77%, while task performance increases from 18.2% to 34.0%. Similarly, for Deepseek on GSM8K, PGR increases from 51.39% to 83.33%, while performance enhances from 62.39% to 71.11%. On the MATH dataset, Deepseek shows a notable improvement, with PGR rising from 65.85% to 119.51%, and task performance increasing from 16.8% to 21.2%.

4.4 Further Improvement from Enhanced Question Quality

As further illustrated in Figure 4(b), the refinement process in Stage II effectively enhances the quality of the training data, particularly in terms of difficulty and diversity, leading to significant improvements in model performance. Specifically, for the Llama 3 series, the strong model achieves a peak PGR of 120.50% on the GSM8K dataset, reflecting an additional 21.94% improvement compared to the finetuned strong model in Stage I, corresponding to a performance of 81.50%. On the MATH dataset, we observe a peak PGR of 121.28%, with a further increase of 8.51% compared to Stage I, reaching 35.2% on task performance.

For the Deepseek series, the strong model attains a peak PGR of 90.04% on GSM8K, marking an additional 6.71% improvement over Stage I, with a corresponding finetuned performance of 72.94%. On MATH, the peak PGR reaches 126.83%, demonstrating a further increase of 7.32% compared to Stage I, with task performance reaching 21.8%.

5 Analysis

5.1 The Impact of Excessive Filtering on Supervision Quality

As shown in Figure 3, label correctness increases as model uncertainty decreases. However, in preliminary experiments during Stage I, we observed an intriguing trend: while performance improves initially as uncertainty decreases, it starts to deteriorate after a certain threshold. This suggests that other factors, beyond supervision quality, influence weak-to-strong generalization, and existing filtering methods may have inherent limitations.

Reduction in Data Difficulty Figure 5 shows that increasing the filtering threshold leads to a decrease in average difficulty, with fewer hard questions (Levels 4-5) remaining in the dataset. These harder questions represent areas where the weak model is less confident, suggesting they are beyond its current capabilities. In contrast, easier questions (Levels 1-2), where the model is more confident, dominate the dataset. This results in a less challenging training set, hindering the model’s ability to generalize to more difficult problems and contributing to data degeneration.

Shift in Data Diversity As shown in Figure 6, filtering also causes a significant shift in the diver-

![Impact of filtering threshold on question difficulty distribution. As the threshold increases, the proportion of difficult questions (Levels 4-5) decreases, while easier questions (Levels 1-2) increase, resulting in a decline in average difficulty from 3.48 to 2.66.](page_1012_120_388_312.png)

Figure 5: Impact of filtering threshold on question difficulty distribution. As the threshold increases, the proportion of difficult questions (Levels 4-5) decreases, while easier questions (Levels 1-2) increase, resulting in a decline in average difficulty from 3.48 to 2.66.

![Changes in topic distribution across filtering thresholds for three representative mathematical categories. Filtering causes shifts in topic distribution, with minor categories seeing more reductions.](page_1012_484_388_312.png)

Figure 6: Changes in topic distribution across filtering thresholds for three representative mathematical categories. Filtering causes shifts in topic distribution, with minor categories seeing more reductions.
sity of questions. For instance, the Counting and Probability section drops from 10.79% to 4.31%, reflecting changes in the model’s uncertainty. This shift in data diversity impacts the variety of question types, reducing exposure to harder topics. The complete trends and numerical results across all categories are provided in Appendix D.1.

Once the filtering threshold surpasses a certain point, performance degrades due to the exclusion of important, challenging data. While reducing label uncertainty can improve performance, excessive filtering diminishes the dataset’s diversity, particularly regarding difficulty and topic variety. This limits the model’s ability to generalize effectively, leading to degeneration in its overall performance.

5.2 The Robust Effectiveness of Data Refinement in Stage II

To address excessive filtering, we propose a strategy that balances uncertainty-based filtering with the preservation of question quality, including difficulty and diversity. In Stage II, we regenerate answers for discarded questions from Stage I using the finetuned model, filtering them by uncertainty before adding low-uncertainty samples to the dataset.

As shown in Figure 4(a), Stage II consistently improves performance across all filtering thresholds, demonstrating the effectiveness of our framework in recovering lost data and boosting performance.

Figure 7 shows recovery in both difficulty and diversity, with the refined dataset closely resembling the original. For Llama 3 on MATH, PGR increases from 112.77% to 121.28%, and performance rises from 34.4% to 35.2%. Similar results are observed in Figure 4, highlighting the framework’s robustness across models and datasets.

Additionally, Figure 4 demonstrates that even models with initially lower performance show significant improvements. For the Deepseek series on MATH, the performance gap between thresholds narrows in Stage II, indicating that the framework effectively recovers discarded data from over-filtered scenarios while refining fewer under-filtered questions.

5.3 The Importance of Label Filtering in Stage II

In Stage II, we focus on enhancing question quality and mitigating degeneration by using the finetuned model to generate answers for discarded questions from Stage I. Instead of adding all generated answers back, we apply an uncertainty-based filter to ensure only reliable answers are reintegrated, preventing the inclusion of low-quality data.

Table 1 summarizes the results of the ablation study comparing the framework with and without the filtering process, using the Llama 3 model series on the GSM8K dataset.

<table>
  <tr>
    <th></th>
    <th>Origin</th>
    <th>With Filter</th>
    <th>Without Filter</th>
  </tr>
  <tr>
    <td>Stage I-50%</td>
    <td>78.99</td>
    <td>80.89 (+1.90)</td>
    <td>78.31 (-0.68)</td>
  </tr>
  <tr>
    <td>Stage I-60%</td>
    <td>80.07</td>
    <td>81.50 (+1.43)</td>
    <td>78.84 (-1.23)</td>
  </tr>
  <tr>
    <td>Stage I-70%</td>
    <td>80.28</td>
    <td>81.19 (+0.91)</td>
    <td>80.28 (+0.00)</td>
  </tr>
  <tr>
    <td>Stage I-80%</td>
    <td>80.06</td>
    <td>80.74 (+0.68)</td>
    <td>79.59 (-0.47)</td>
  </tr>
</table>

Table 1: The impact of With vs. Without label filtering in Stage II on Weak-to-Strong Generalization.

As shown in Table 1, appending all generated samples without filtering leads to performance degradation, highlighting that indiscriminate inclusion reduces supervision quality. The uncertainty-based filter ensures optimal supervision and question quality, which are critical for effective weak-to-strong reasoning generalization.

![Bar chart showing topic distribution comparison in Stage II under different thresholds.](page_1012_120_388_246.png)
(a) Topic distribution comparison in Stage II under different thresholds.

![Bar chart showing distribution of difficulty levels and average difficulty scores in Stage II.](page_1012_370_388_246.png)
(b) Distribution of difficulty levels and average difficulty scores in Stage II.

Figure 7: Difficulty and diversity analysis in Stage II (GSM8K, Llama 3, Threshold-70%), showing improved preservation of question quality.
<table>
  <tr>
    <th></th>
    <th>Chain-of-Thought</th>
    <th>Direct Answer</th>
  </tr>
  <tr>
    <th>GSM8K</th>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>74.8</td>
    <td>14.6</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>80.36</td>
    <td>30.93</td>
  </tr>
  <tr>
    <td>Weak-to-Strong</td>
    <td>75.2</td>
    <td>13.64</td>
  </tr>
  <tr>
    <td>PGR</td>
    <td>7.19%</td>
    <td>-5.87%(-13.06%)</td>
  </tr>
  <tr>
    <th>MATH</th>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>23.8</td>
    <td>14.6</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>33.2</td>
    <td>30.93</td>
  </tr>
  <tr>
    <td>Weak-to-Strong</td>
    <td>27.2</td>
    <td>11.4</td>
  </tr>
  <tr>
    <td>PGR</td>
    <td>36.17%</td>
    <td>-31.8%(-76.97%)</td>
  </tr>
</table>

Table 2: Performance comparison between chain-of-thought and direct answer approaches in weak-to-strong generalization on GSM8K and MATH datasets with Deepseek series.

5.4 The Role of Chain-of-Thought in Weak-to-Strong Reasoning

In contrast to the original weak-to-strong generalization framework proposed by (Burns et al., 2023), where all tasks are classification-based, reasoning tasks like GSM8K and MATH consist of open-ended questions that lack definitive answer sets. Previous work has utilized chain-of-thought prompting to enhance performance (Guo and Yang, 2024; Yang et al., 2024b). This raises the question: Can weak-to-strong generalization remain effective without chain-of-thought prompting?

To explore this, we replicate the same baseline settings, comparing using chain-of-thought answers to manually constructed direct answers. The results are shown in Table 2.

When omitting chain-of-thought prompting, we fail to observe generalization in strong models, as finetuned strong models perform worse than their weak teachers. This occurs because step-by-step reasoning enables strong models to verify intermediate steps, learn question decomposition, and mitigate harm from incorrect labels. In contrast, direct answers propagate errors without instructive pathways and cause greater damage as strong models learn only incorrect outcomes. We conclude chain-of-thought is essential for weak-to-strong generalization in reasoning tasks and may benefit other domains.

5.5 Is MATH 500 Precise Enough Compared to MATH 5000?

As introduced in Section 2, the Performance Gap Recovered (PGR) quantifies the effectiveness of weak-to-strong generalization by comparing the performances of three models: weak model, strong ceiling model, and finetuned strong model. Our initial evaluations used a subset of 500 test samples (MATH500). Given this relatively small sample size, performance variations of up to 0.2 points per test sample were observed. This variation could be particularly significant when the performance gap between weak and strong ceiling models is small, potentially affecting the reliability of our results.

To validate our findings, we conducted additional evaluations on the untrained test set using models from the DeepSeek series. The results are presented in Table 3.

<table>
  <tr>
    <th>Model</th>
    <th>MATH500</th>
    <th>MATH5000</th>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>11.4</td>
    <td>9.34</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>19.6</td>
    <td>20.12</td>
  </tr>
  <tr>
    <th colspan="3">Stage I Models</th>
  </tr>
  <tr>
    <td>Stage I-Threshold-30%</td>
    <td>21.2 (119.51%)</td>
    <td>19.96 (98.52%)</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-40%</td>
    <td>19.6 (100.00%)</td>
    <td>17.38 (76.44%)</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-50%</td>
    <td>17.6 (75.61%)</td>
    <td>16.84 (69.57%)</td>
  </tr>
  <tr>
    <th colspan="3">Stage II Models</th>
  </tr>
  <tr>
    <td>Stage I-30% + Stage II-30%</td>
    <td>21.4 (121.95%)</td>
    <td>21.3 (110.95%)</td>
  </tr>
  <tr>
    <td>Stage I-30% + Stage II-40%</td>
    <td>21.8 (126.83%)</td>
    <td>20.9 (107.24%)</td>
  </tr>
  <tr>
    <td>Stage I-30% + Stage II-50%</td>
    <td>19.4 (97.56%)</td>
    <td>19.48 (94.06%)</td>
  </tr>
  <tr>
    <td>Stage I-40% + Stage II-30%</td>
    <td>20.4 (109.76%)</td>
    <td>19.62 (95.36%)</td>
  </tr>
  <tr>
    <td>Stage I-40% + Stage II-40%</td>
    <td>19.8 (102.44%)</td>
    <td>19.46 (93.88%)</td>
  </tr>
  <tr>
    <td>Stage I-40% + Stage II-50%</td>
    <td>17.4 (73.17%)</td>
    <td>17.62 (76.81%)</td>
  </tr>
  <tr>
    <td>Stage I-50% + Stage II-30%</td>
    <td>20.6 (112.20%)</td>
    <td>19.98 (98.70%)</td>
  </tr>
  <tr>
    <td>Stage I-50% + Stage II-40%</td>
    <td>20.6 (112.20%)</td>
    <td>20.5 (103.53%)</td>
  </tr>
  <tr>
    <td>Stage I-50% + Stage II-50%</td>
    <td>19.4 (97.56%)</td>
    <td>18.8 (87.76%)</td>
  </tr>
  <tr>
    <td>Stage I-50% + Stage II-60%</td>
    <td>18.6 (87.80%)</td>
    <td>18.38 (83.86%)</td>
  </tr>
</table>

Table 3: Performance comparison between MATH500 and MATH5000 test sets. Numbers in parentheses represent PGR values.

The results in Table 3 demonstrate that our framework achieves consistent performance across both MATH500 and MATH5000. While the absolute accuracy values remain similar, the slightly lower PGR on MATH5000 can be attributed to the weaker baseline performance of the weak model. However, this difference does not significantly impact our framework’s effectiveness. These findings confirm that MATH500 serves as a reliable representative subset for evaluating model performance using PGR, and our framework maintains its efficacy across different evaluation scales.

5.6 Exploring the Potential for Further Iterative Refinement

While our current framework demonstrates considerable effectiveness, we recognize that additional iterations could further improve question quality, thereby enhancing overall framework performance. Specifically, the refinement process in Stage II—where discarded questions are re-
<table>
  <tr>
    <th></th>
    <th>Accuracy</th>
    <th>PGR</th>
  </tr>
  <tr>
    <th>GSM8K</th>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Baseline</td>
    <td>62.39</td>
    <td>51.39%</td>
  </tr>
  <tr>
    <td>Stage I</td>
    <td>71.11</td>
    <td>83.33% (+31.94%)</td>
  </tr>
  <tr>
    <td>Stage II</td>
    <td>72.94</td>
    <td>90.04% (+38.65%)</td>
  </tr>
  <tr>
    <td>Stage Exp-Threshold-80%</td>
    <td>72.26</td>
    <td>87.55%</td>
  </tr>
  <tr>
    <td>Stage Exp-Threshold-90%</td>
    <td>72.93</td>
    <td>90.00%</td>
  </tr>
  <tr>
    <td>Stage Exp-Threshold-100%</td>
    <td><u>73.77</u></td>
    <td><u>93.08% (+41.69%)</u></td>
  </tr>
  <tr>
    <th>MATH</th>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Baseline</td>
    <td>16.8</td>
    <td>65.85%</td>
  </tr>
  <tr>
    <td>Stage I</td>
    <td>21.2</td>
    <td>119.51% (+53.66%)</td>
  </tr>
  <tr>
    <td>Stage II</td>
    <td>21.8</td>
    <td>126.83% (+60.98%)</td>
  </tr>
  <tr>
    <td>Stage Exp-Threshold-50%</td>
    <td>21.4</td>
    <td>120.71%</td>
  </tr>
  <tr>
    <td>Stage Exp-Threshold-40%</td>
    <td>21.2</td>
    <td>119.51%</td>
  </tr>
  <tr>
    <td>Stage Exp-Threshold-30%</td>
    <td><u>22.4</u></td>
    <td><u>134.15% (+68.3%)</u></td>
  </tr>
</table>

Table 4: Performance comparison of iterative refinement on GSM8K and MATH datasets (Deepseek model). Best results are underlined.

covered and answered using the finetuned strong model—holds significant potential for further improvement. This iterative process, as the model’s ability improves, may offer a pathway for continuous enhancement of question quality.

We introduce an additional iteration, which we term Stage Exp, aimed at refining discarded questions by utilizing finetuned strong model in Stage II to generate answers, and append samples to the existing dataset after uncertainty filtering. Due to computational limits, Stage Exp experiments focused on Deepseek series with best configurations for GSM8K and MATH.

As shown in Table 4, our framework demonstrates a promising potential for further refinement by leveraging the power of finetuned strong models to iteratively enhance discarded questions. However, it is important to acknowledge that the selection of an optimal threshold for these further iterations remains an open question, which we intend to address in future work.

6 Related Work

6.1 AI Deceptions

A persistent challenge in weak-to-strong generalization is AI deception, where strong models overfit to noisy labels from weak models, hindering their ability to generalize to complex samples (Yang et al., 2024a). A similar issue in reinforcement learning from human feedback (RLHF) is identified by Wen et al. (2024), where models mislead human evaluators. To address this, they propose the "U-SOPHISTRY" pipeline.

This behaviour is akin to model sycophancy, where models align with human feedback at the expense of accuracy. Early work by Cotra (2021) and Perez et al. (2023) shows models often aim to please users. Sharma et al. (2024) attributes this to human preference biases. Solutions such as synthetic data (Wei et al., 2023) and pinpoint tuning (Chen et al., 2024) aim to mitigate sycophancy, while Sicilia et al. (2024) links it to model uncertainty.

6.2 Weak-to-Strong Generalization

Weak-to-strong generalization, introduced by OpenAI (Burns et al., 2023), has led to advancements in model training and supervision. Recent studies explore ensemble learning to improve labels by integrating predictions from smaller models (Liu and Alahi, 2024; Agrawal et al., 2024; Cui et al., 2024). In terms of training methodologies, Dong et al. (2024) replaces traditional sample-label pairs with concept vectors to enhance learning representations, while Guo and Yang (2024) introduces filtering mechanisms and confidence-based reweighting strategies. Furthermore, a two-stage learning framework presented in Yang et al. (2024b) iteratively refines training data. Zhou et al. (2024) enhances strong model with weak test-time guidance, and Lyu et al. (2024) proposes a multi-agent contrastive preference optimization approach. Theoretical foundations of weak-to-strong generalization have been studied (Lang et al., 2024; Charikar et al., 2024; Wu and Sahai, 2024). Safety considerations are also highlighted, addressing AI safety implications within weak-to-strong frameworks (Yang et al., 2024a; Zhao et al., 2024; Ye et al., 2024).

7 Conclusion

In this paper, we introduce a two-stage training framework to enhance weak-to-strong generalization through mitigating overfitting. By focusing on both supervision and question quality, we demonstrate that traditional data filtering methods, while improving supervision, can reduce question difficulty and diversity. Our framework mitigates this by relabeling discarded questions using the fine-tuned strong model, maintaining both supervision accuracy and question quality.

Experiments on the GSM8k and MATH benchmarks demonstrate that our approach significantly outperforms conventional weak-to-strong generalization methods, improving the performance gap recovered (PGR). This validates the effectiveness of our framework in addressing overfitting and enhancing model capabilities on challenging tasks.
Limitations

Our experiments demonstrate strong performance on mathematical reasoning tasks, though the framework’s effectiveness remains to be validated across other domains. Through extensive experimentation, we identified optimal confidence thresholds for filtering model predictions. However, these thresholds vary significantly across different tasks and datasets, making automatic threshold selection an important direction for future research. Additionally, the computational overhead of our two-stage finetuning approach, particularly in the second stage, may pose scalability challenges for large-scale applications or real-time scenarios.

Acknowledgment

This work was supported by the National Key Research and Development Program of China (No.2022ZD0160102).

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A Dataset details

A.1 Dataset Statistics

For the original question set used in GSM8K and MATH, we followed the methodology of Yang et al. (2024b), adopting the same training set for both datasets. Specifically, we used their dataset \( D_2 \), which was employed for training the Llama 2 70B model. For GSM8K, the dataset consists of 7,000 samples, while for MATH, the dataset comprises 6,000 samples.

For evaluation, we utilized the original evaluation set for GSM8K and the test set from Lightman et al. (2024), which contains 500 samples. We compared the model’s performance on the 500 samples subset with that on the original test dataset, with details provided in Appendix 5.5.

A.2 Implementation Details

For answer generation within the framework, we utilize chain-of-thought (CoT) prompting, as its necessity has been outlined in Section 5.4. In Stage I, answers are generated using zero-shot CoT prompting for the weak models in the Deepseek series. However, for the Llama 3 series, we observed that the Llama 3 SB Instruct model performed below expectations, prompting us to switch from zero-shot to one-shot CoT to enhance its performance.

For sampling parameters, we generate answers with a temperature of 0.6 and top-p of 0.9 for uncertainty-based filtering to ensure diverse and coherent outputs, while using greedy decoding during evaluation to enhance stability.

In both Stage II and the experimental Stage Exp, discussed in Section 5.5, all answers are generated using zero-shot prompting. During the filtering process, after excluding answers based on model confidence, we also discard responses that fail to generate valid answers or do not adhere to the CoT format.

A.3 Prompting Template

To better evaluate and compare the mathematical reasoning capabilities of different models, we designed specific prompting templates. For Stage I answer generation, we employ chat-style templates to facilitate more natural responses, while in Stage II answer generation and evaluation, we utilize the direct template for standardization.

We designed the following prompting templates for different models, where [INPUT] denotes the mathematical question to be solved.
Direct Template:

Direct Template:
Prompt:
Question: [INPUT]
Answer:

Llama 3 GSM8K Template:

Llama 3 GSM8K Template:
Prompt:
<begin_of_text>
<lstart_header_idl>user<lend_header_idl>
Please additionally write your final answer with ####, like the example:
Question: Greg has his own dog walking business. He charges $20 per dog plus $1 per minute per dog for walking the dog. If he walks one dog for 10 minutes, two dogs for 7 minutes and three dogs for 9 minutes, how much money, in dollars, does he earn?
Answer: Greg earns $20 + $1 x 10 minutes = $21 for walking the first dog. He earns $20 + $1 x 7 minutes = $27 for walking the second dog. He earns $20 + $1 x 9 minutes = $29 for walking the third dog. Therefore, Greg earns $21 + $27 + $29 = $77 for walking the three dogs. #### 77
Question:
Answer:
<leot_idl>
<lstart_header_idl>assistant<lend_header_idl>

Llama 3 MATH Template:

Llama 3 MATH Template:
Prompt:
<begin_of_text>
<lstart_header_idl>user<lend_header_idl>
Answer the math question step by step. Our answers need to end with 'The answer is '.
Question: [INPUT]
Answer: Let’s think step by step.
<leot_idl>
<lstart_header_idl>assistant<lend_header_idl>

DeepSeek Templates:

DeepSeek Templates:
Prompt:
<begin_of_sentence>
User: Question: [INPUT]
Please reason step by step, and put your final answer after 'The answer is: '.
Assistant:

B Training Details

For the supervised finetuning in our framework, we perform full-parameter finetuning on the strong model. The finetuning is carried out with a learning rate of \(110^{-5}\), a warmup ratio of 0.1, and a cosine learning rate scheduler. We use a batch size of 128 and train for 2 epochs on both the GSM8K and MATH datasets. The implementation is based on the LlamaFactory (Zheng et al., 2024) framework and all experiments are conducted using 64 H100 80GB GPUs to ensure efficient processing and model optimization.

C Additional Analysis

C.1 Theoretical Analysis

The weak-to-strong generalization phenomenon can be theoretically explained through two key mechanisms: pseudolabel correction and coverage expansion, as demonstrated by Lang et al. (2024). These mechanisms enable the student model to both correct erroneous labels from the weak teacher and generalize to samples where the teacher lacks confidence.

Let us consider a student model \( f \), a covered subset \( S \) partitioned into correct samples \( S^{good} \) and incorrect samples \( S^{bad} \), and the weak teacher’s error rate \( \alpha \). We can establish the relationship between the gold error \( \mathrm{err}(f, y|S) \) and the weak error \( \mathrm{err}(f, \hat{y}|S) \) of the student model.

Given that \( \mathcal{M}'(S^{good}, \mathcal{F}) \) satisfies \((c, q)\)-expansion on \((S^{bad}, S^{good})\) where \( q < \frac{3}{4}(1 - 2\alpha) \), and for an optimal classifier \( f \) whose probability of prediction errors or non-robustness is bounded by \((1 - \alpha + 3c\alpha)/4\), we can establish the following bound:

\[
\mathrm{err}(f, y|S) \leq \frac{2\alpha}{1 - 2\alpha} \mathbb{P}(\overline{\mathcal{R}}(f)|S) + \mathrm{err}(f, \hat{y}|S) + \alpha \left(1 - \frac{3}{2}c\right).
\]
This bound demonstrates that when the expansion coefficient \( c \) is sufficiently large and both \( \mathrm{err}(f, \tilde{y}|S) \) and \( \mathbb{P}(\overline{R}(f)|S) \) are minimal, the true error \( \mathrm{err}(f, y|S) \) can be significantly lower than the weak teacher’s error rate \( \alpha \), indicating successful pseudolabel correction.

For the uncovered set \( T \), assuming \( \mathcal{M}(T, \mathcal{F}) \) satisfies \((c, q)\)-expansion on \((S^{good}, T)\) and \( \mathcal{M}'(T, \mathcal{F}) \) satisfies \((c, q)\)-expansion on \((S^{bad}, T)\), we can derive another bound. For a classifier \( f \in \mathcal{F} \) that demonstrates good fit to weak labels on \( S \) and maintains robustness on \( T \) such that \( \mathrm{err}(f, \tilde{y}|S) + \mathbb{P}(\overline{R}(f)|T) < c(1 - q - \alpha) \), we have:

\[
\mathrm{err}(f, y|T) \leq \left( 1 + \frac{\alpha}{1-2\alpha} \right) \mathbb{P}(\overline{R}(f)|T) + \max \left( q, \frac{\mathrm{err}(g, y|S) - c\alpha}{c(1-2\alpha)} \right).
\]

This bound becomes particularly tight when \( f \) exhibits strong performance on weak labels in \( S \) and maintains robustness across \( T \), resulting in small values for both \( \mathrm{err}(f, \tilde{y}|S) \) and \( \mathbb{P}(\overline{R}(f)|T) \). Combined with the previous bound on \( \mathrm{err}(f, y|S) \), these results theoretically justify the student model’s capacity to surpass its weak teacher.

In our framework, the filtering mechanism serves to reduce the weak teacher’s error rate \( \alpha \) by excluding low-confidence samples, thereby improving supervision quality and consequently reducing \( \mathrm{err}(f, y|S) \). However, this improvement relies on the assumption that sets \( S \) and \( T \) maintain similar distributional characteristics for effective generalization. As the filtering threshold increases, the shrinking of set \( S \) and expansion of set \( T \) can lead to distributional shifts that violate this assumption. To address this challenge, Stage II of our framework employs the finetuned model to generate predictions for previously discarded questions, selectively reincorporating high-confidence predictions into the dataset. This approach effectively reduces the weak error rate \( \alpha \) while preserving distributional similarity, ultimately enhancing weak-to-strong generalization.

C.2 Filtering Implications on Other Datasets

To validate the broader applicability of our framework, we conducted additional experiments on the SciQ classification task (Welbl et al., 2017) following the experimental protocol from (Burns et al., 2023). We used Qwen-1.8B as the weak supervisor and evaluated two stronger student models: Qwen-7B and Qwen-14B, employing absolute logits filtering with a threshold of 0.6. For consistency with prior work, we aligned hyperparameters with those from OpenAI’s official repository. The results are presented in Table 5.

<table>
  <tr>
    <th></th>
    <th>Accuracy</th>
    <th>PGR</th>
  </tr>
  <tr>
    <td>Qwen-7B</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>83.8</td>
    <td>/</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>90.0</td>
    <td>/</td>
  </tr>
  <tr>
    <td>Conventional Weak-to-Strong</td>
    <td>87.3</td>
    <td>56.5%</td>
  </tr>
  <tr>
    <td>Our Stage I</td>
    <td>87.7</td>
    <td>62.9%(+6.4%)</td>
  </tr>
  <tr>
    <td>Our Stage II</td>
    <td>87.9</td>
    <td>66.1%(+9.6%)</td>
  </tr>
  <tr>
    <td>Qwen-14B</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>83.8</td>
    <td>/</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>93.5</td>
    <td>/</td>
  </tr>
  <tr>
    <td>Conventional Weak-to-Strong</td>
    <td>88.6</td>
    <td>49.5%</td>
  </tr>
  <tr>
    <td>Our Stage I</td>
    <td>89.4</td>
    <td>57.7%(+8.2%)</td>
  </tr>
  <tr>
    <td>Our Stage II</td>
    <td>89.7</td>
    <td>60.8%(+11.3%)</td>
  </tr>
</table>

Table 5: Performance of our framework on the SciQ classification task with Qwen model series.

Our framework demonstrates consistent improvements across both stages, even in classification tasks distinct from mathematical reasoning. For example, Qwen-14B’s PGR improved by 8.3% from Stage I to Stage II, while accuracy increased from 0.894 to 0.897. These results suggest that our two-stage approach effectively generalizes to diverse task formats and model scales, balancing supervision quality and question utility to mitigate overfitting. The incremental gains across stages further underline the importance of addressing both label noise and data degeneration in weak-to-strong generalization.

We have also conducted experiments on the Paws dataset (Zhang et al., 2019) and the justice subset of the Ethics dataset (Hendrycks et al., 2023), both using fine-tuned Qwen-1.8B as weak supervisor and Qwen-7B and Qwen-14B as strong students. Tables 6 and 7 present the results, where "Stage I" denotes accuracy after the first filtering stage, "Stage I + Stage II" indicates final accuracy after both stages, and PGR measures performance gain relative to strong ceiling. For baselines, Stage I is inapplicable (left blank), and final accuracy is reported in "Stage I + Stage II".

These results confirm that our method demonstrates efficacy across different task types. Absolute logits filtering thresholds were systematically varied (60%–90%), with optimal gains observed at 80% for Paws (51.49% PGR for Qwen-14B) and 90% for Ethics (46.48% PGR for Qwen-14B). The improvements, while consistent, are less pronounced than in mathematical reasoning tasks, likely due to the reliance on absolute logits for thresholding in non-reasoning contexts.
<table>
  <tr>
    <th>Approach</th>
    <th>Stage I</th>
    <th>Stage I + Stage II</th>
    <th>PGR</th>
  </tr>
  <tr>
    <td>Qwen-7B</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>90.35</td>
    <td></td>
    <td>-</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>93.1</td>
    <td></td>
    <td>100%</td>
  </tr>
  <tr>
    <td>Conventional Weak-to-Strong</td>
    <td>91.39</td>
    <td>91.39</td>
    <td>37.82%</td>
  </tr>
  <tr>
    <td>Threshold 60%</td>
    <td>91.31</td>
    <td>91.39</td>
    <td>37.82%</td>
  </tr>
  <tr>
    <td>Threshold 70%</td>
    <td>91.28</td>
    <td>91.46</td>
    <td>40.36%</td>
  </tr>
  <tr>
    <td>Threshold 80%</td>
    <td>91.31</td>
    <td>91.39</td>
    <td>37.82%</td>
  </tr>
  <tr>
    <td>Threshold 90%</td>
    <td>90.93</td>
    <td>90.85</td>
    <td>18.18%</td>
  </tr>
  <tr>
    <td>Qwen-14B</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>90.35</td>
    <td></td>
    <td>-</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>95.4</td>
    <td></td>
    <td>100%</td>
  </tr>
  <tr>
    <td>Conventional Weak-to-Strong</td>
    <td>92.60</td>
    <td>92.60</td>
    <td>44.55%</td>
  </tr>
  <tr>
    <td>Threshold 60%</td>
    <td>92.45</td>
    <td>92.58</td>
    <td>44.16%</td>
  </tr>
  <tr>
    <td>Threshold 70%</td>
    <td>92.48</td>
    <td>92.73</td>
    <td>47.13%</td>
  </tr>
  <tr>
    <td>Threshold 80%</td>
    <td>92.90</td>
    <td>92.95</td>
    <td>51.49%</td>
  </tr>
  <tr>
    <td>Threshold 90%</td>
    <td>92.29</td>
    <td>92.60</td>
    <td>44.55%</td>
  </tr>
</table>

Table 6: Performance on the Paws dataset with Qwen model series.

<table>
  <tr>
    <th>Approach</th>
    <th>Stage I</th>
    <th>Stage I + Stage II</th>
    <th>PGR</th>
  </tr>
  <tr>
    <td>Qwen-7B</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>71.30</td>
    <td></td>
    <td>-</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>83.09</td>
    <td></td>
    <td>100%</td>
  </tr>
  <tr>
    <td>Conventional Weak-to-Strong</td>
    <td>75.80</td>
    <td></td>
    <td>38.17%</td>
  </tr>
  <tr>
    <td>Threshold 60%</td>
    <td>75.68</td>
    <td>75.87</td>
    <td>38.76%</td>
  </tr>
  <tr>
    <td>Threshold 70%</td>
    <td>76.70</td>
    <td>76.70</td>
    <td>45.80%</td>
  </tr>
  <tr>
    <td>Threshold 80%</td>
    <td>75.82</td>
    <td>75.92</td>
    <td>39.19%</td>
  </tr>
  <tr>
    <td>Threshold 90%</td>
    <td>76.31</td>
    <td>76.31</td>
    <td>42.49%</td>
  </tr>
  <tr>
    <td>Qwen-14B</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>71.30</td>
    <td></td>
    <td>-</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>86.06</td>
    <td></td>
    <td>100%</td>
  </tr>
  <tr>
    <td>Conventional Weak-to-Strong</td>
    <td>77.53</td>
    <td>77.53</td>
    <td>42.21%</td>
  </tr>
  <tr>
    <td>Threshold 60%</td>
    <td>77.72</td>
    <td>77.72</td>
    <td>43.50%</td>
  </tr>
  <tr>
    <td>Threshold 70%</td>
    <td>78.50</td>
    <td>77.82</td>
    <td>44.17%</td>
  </tr>
  <tr>
    <td>Threshold 80%</td>
    <td>77.63</td>
    <td>77.77</td>
    <td>43.83%</td>
  </tr>
  <tr>
    <td>Threshold 90%</td>
    <td>77.39</td>
    <td>78.16</td>
    <td>46.48%</td>
  </tr>
</table>

Table 7: Performance on the Ethics dataset (justice subset) with Qwen model series.

D Additional Experimental Results

D.1 Detailed Analysis of Section Diversity Shifts

In this appendix, we analyze how filtering thresholds affect section distribution in both stages of our framework. As shown in Figure 8 for Stage I, increasing the filtering threshold leads to a noticeable reduction in several minor categories, negatively impacting the strong model’s ability. For Stage II, Figure 9 demonstrates how Llama 3 MATH recovers some minor categories, revealing the trade-off between filtering accuracy and maintaining category diversity. We provide detailed distributions to illustrate these changes across categories.

D.2 Numeric Results of All Models and Datasets

We present the numerical results for all models and datasets used in the experiments , showcasing the impact of various stages and filtering thresholds on model performance.

![Bar chart showing changes in topic distribution across filtering thresholds for all mathematical categories in Stage I.](page_1012_120_420_320.png)

Figure 8: Changes in topic distribution across filtering thresholds for all mathematical categories in Stage I. (Llama 3 MATH) Filtering causes shifts in topic distribution, with minor categories seeing more reductions.

![Bar chart showing changes in topic distribution across filtering thresholds for all mathematical categories in Stage II.](page_1012_600_420_320.png)

Figure 9: Changes in topic distribution across filtering thresholds for all mathematical categories in Stage II.(Llama 3 MATH Stage I-Threshold-70%) We observe recovery in several minor categories, while sections including algebra, intermediate algebra, prealgebra are also effected by difficulty.
<table>
  <tr>
    <th></th>
    <th>Accuracy</th>
    <th>Performance gap recovered(PGR)</th>
  </tr>
  <tr>
    <th colspan="3">Basic Settings</th>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>74.8%</td>
    <td>0%</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>80.36%</td>
    <td>100%</td>
  </tr>
  <tr>
    <td>Conventional Weak-to-Strong</td>
    <td>75.2%</td>
    <td>7.19%</td>
  </tr>
  <tr>
    <th colspan="3">Stage I</th>
  </tr>
  <tr>
    <td>Stage I-Threshold-30%</td>
    <td>79.37%</td>
    <td>82.19%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-40%</td>
    <td>79.51%</td>
    <td>84.71%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-50%</td>
    <td>78.99%</td>
    <td>75.36%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-60%</td>
    <td>80.07%</td>
    <td>94.78%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-70%</td>
    <td>80.28%</td>
    <td>98.56%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-80%</td>
    <td>80.06%</td>
    <td>94.60%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-90%</td>
    <td>80.13%</td>
    <td>95.86%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-100%</td>
    <td>78.16%</td>
    <td>60.43%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Stage I Threshold-50%</th>
  </tr>
  <tr>
    <td>Stage I-50% + Stage II-50%</td>
    <td>80.28%</td>
    <td>98.56%</td>
  </tr>
  <tr>
    <td>Stage I-50% + Stage II-60%</td>
    <td>80.89%</td>
    <td>109.53%</td>
  </tr>
  <tr>
    <td>Stage I-50% + Stage II-70%</td>
    <td>79.62%</td>
    <td>86.69%</td>
  </tr>
  <tr>
    <td>Stage I-50% + Stage II-80%</td>
    <td>79.37%</td>
    <td>82.19%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Stage I Threshold-60%</th>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-50%</td>
    <td>80.28%</td>
    <td>98.56%</td>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-60%</td>
    <td>81.50%</td>
    <td>120.50%</td>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-70%</td>
    <td>81.04%</td>
    <td>112.23%</td>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-80%</td>
    <td>81.34%</td>
    <td>117.63%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Stage I Threshold-70%</th>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-60%</td>
    <td>80.89%</td>
    <td>109.53%</td>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-70%</td>
    <td>80.36%</td>
    <td>100.00%</td>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-80%</td>
    <td>81.19%</td>
    <td>114.93%</td>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-90%</td>
    <td>80.89%</td>
    <td>109.53%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Stage I Threshold-80%</th>
  </tr>
  <tr>
    <td>Stage I-80% + Stage II-70%</td>
    <td>80.43%</td>
    <td>101.26%</td>
  </tr>
  <tr>
    <td>Stage I-80% + Stage II-80%</td>
    <td>80.33%</td>
    <td>99.46%</td>
  </tr>
  <tr>
    <td>Stage I-80% + Stage II-90%</td>
    <td>80.45%</td>
    <td>101.62%</td>
  </tr>
  <tr>
    <td>Stage I-80% + Stage II-100%</td>
    <td>80.74%</td>
    <td>106.83%</td>
  </tr>
</table>

Table 8: Llama3 GSM8k
<table>
  <tr>
    <th></th>
    <th>Accuracy</th>
    <th>Performance gap recovered(PGR)</th>
  </tr>
  <tr>
    <th colspan="3">Basic Settings</th>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>23.8%</td>
    <td>0%</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>33.2%</td>
    <td>100%</td>
  </tr>
  <tr>
    <td>Conventional Weak-to-Strong</td>
    <td>27.2%</td>
    <td>36.17%</td>
  </tr>
  <tr>
    <th colspan="3">Stage I</th>
  </tr>
  <tr>
    <td>Stage I-Threshold-30%</td>
    <td>27.2%</td>
    <td>36.17%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-40%</td>
    <td>29.8%</td>
    <td>63.83%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-50%</td>
    <td>30.0%</td>
    <td>65.96%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-60%</td>
    <td>31.4%</td>
    <td>80.85%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-70%</td>
    <td>34.4%</td>
    <td>112.77%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-80%</td>
    <td>33.2%</td>
    <td>100.00%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-90%</td>
    <td>32.6%</td>
    <td>93.62%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-100%</td>
    <td>22.6%</td>
    <td>-12.77%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Stage I Threshold-60%</th>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-50%</td>
    <td>27.0%</td>
    <td>34.04%</td>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-60%</td>
    <td>30.6%</td>
    <td>72.34%</td>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-70%</td>
    <td>32.4%</td>
    <td>91.49%</td>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-80%</td>
    <td>32.4%</td>
    <td>91.49%</td>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-90%</td>
    <td>29.0%</td>
    <td>55.32%</td>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-100%</td>
    <td>30.7%</td>
    <td>73.40%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Stage I Threshold-70%</th>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-60%</td>
    <td>32.2%</td>
    <td>89.36%</td>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-70%</td>
    <td>32.4%</td>
    <td>91.49%</td>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-80%</td>
    <td>35.2%</td>
    <td>121.28%</td>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-90%</td>
    <td>34.2%</td>
    <td>110.64%</td>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-100%</td>
    <td>33.2%</td>
    <td>100.00%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Stage I Threshold-80%</th>
  </tr>
  <tr>
    <td>Stage I-80% + Stage II-70%</td>
    <td>30.0%</td>
    <td>65.96%</td>
  </tr>
  <tr>
    <td>Stage I-80% + Stage II-80%</td>
    <td>32.2%</td>
    <td>89.36%</td>
  </tr>
  <tr>
    <td>Stage I-80% + Stage II-90%</td>
    <td>33.8%</td>
    <td>106.38%</td>
  </tr>
  <tr>
    <td>Stage I-80% + Stage II-100%</td>
    <td>32.8%</td>
    <td>95.74%</td>
  </tr>
</table>

Table 9: Llama 3 MATH
<table>
  <tr>
    <th>Model</th>
    <th>Accuracy</th>
    <th>Performance gap recovered(PGR)</th>
  </tr>
  <tr>
    <th colspan="3">Basic Settings</th>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>48.36%</td>
    <td>0%</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>75.66%</td>
    <td>100%</td>
  </tr>
  <tr>
    <td>conventional Weak-to-Strong</td>
    <td>62.39%</td>
    <td>51.39%</td>
  </tr>
  <tr>
    <th colspan="3">Stage I</th>
  </tr>
  <tr>
    <td>Stage I-Threshold-30%</td>
    <td>68.68%</td>
    <td>74.43%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-40%</td>
    <td>70.96%</td>
    <td>82.78%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-50%</td>
    <td>69.74%</td>
    <td>78.32%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-60%</td>
    <td>70.35%</td>
    <td>80.55%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-70%</td>
    <td>71.11%</td>
    <td>83.33%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-80%</td>
    <td>69.14%</td>
    <td>76.12%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-90%</td>
    <td>68.38%</td>
    <td>73.33%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-100%</td>
    <td>67.55%</td>
    <td>70.29%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Stage I Threshold-40%</th>
  </tr>
  <tr>
    <td>Stage I-40% + Stage II-30%</td>
    <td>72.63%</td>
    <td>88.90%</td>
  </tr>
  <tr>
    <td>Stage I-40% + Stage II-40%</td>
    <td>72.32%</td>
    <td>87.77%</td>
  </tr>
  <tr>
    <td>Stage I-40% + Stage II-50%</td>
    <td>70.58%</td>
    <td>81.39%</td>
  </tr>
  <tr>
    <td>Stage I-40% + Stage II-60%</td>
    <td>72.17%</td>
    <td>87.22%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Stage I Threshold-60%</th>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-60%</td>
    <td>70.28%</td>
    <td>80.29%</td>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-70%</td>
    <td>71.49%</td>
    <td>84.73%</td>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-80%</td>
    <td>70.28%</td>
    <td>80.29%</td>
  </tr>
  <tr>
    <td>Stage I-60% + Stage II-90%</td>
    <td>70.28%</td>
    <td>80.29%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Stage I Threshold-70%</th>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-60%</td>
    <td>72.40%</td>
    <td>88.06%</td>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-70%</td>
    <td>72.94%</td>
    <td>90.04%</td>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-80%</td>
    <td>71.64%</td>
    <td>85.27%</td>
  </tr>
  <tr>
    <td>Stage I-70% + Stage II-90%</td>
    <td>72.55%</td>
    <td>88.61%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Stage I Threshold-80%</th>
  </tr>
  <tr>
    <td>Stage I-80% + Stage II-70%</td>
    <td>70.20%</td>
    <td>80.00%</td>
  </tr>
  <tr>
    <td>Stage I-80% + Stage II-80%</td>
    <td>70.50%</td>
    <td>81.10%</td>
  </tr>
  <tr>
    <td>Stage I-80% + Stage II-90%</td>
    <td>71.47%</td>
    <td>84.65%</td>
  </tr>
  <tr>
    <td>Stage I-80% + Stage II-100%</td>
    <td>70.35%</td>
    <td>80.55%</td>
  </tr>
</table>

Table 10: Deepseek-GSM8K
<table>
  <tr>
    <th>Model</th>
    <th>Accuracy</th>
    <th>Performance gap recovered(PGR)</th>
  </tr>
  <tr>
    <th colspan="3">Basic Settings</th>
  </tr>
  <tr>
    <td>Weak Model</td>
    <td>11.4%</td>
    <td>0%</td>
  </tr>
  <tr>
    <td>Strong Ceiling</td>
    <td>19.6%</td>
    <td>100%</td>
  </tr>
  <tr>
    <td>conventional Weak-to-Strong</td>
    <td>16.8%</td>
    <td>65.85%</td>
  </tr>
  <tr>
    <th colspan="3">Stage I</th>
  </tr>
  <tr>
    <td>Stage I-Threshold-30%</td>
    <td>21.2%</td>
    <td>119.51%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-40%</td>
    <td>19.6%</td>
    <td>100.00%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-50%</td>
    <td>17.6%</td>
    <td>75.61%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-60%</td>
    <td>15.8%</td>
    <td>53.66%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-70%</td>
    <td>16.4%</td>
    <td>60.98%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-80%</td>
    <td>15.0%</td>
    <td>43.90%</td>
  </tr>
  <tr>
    <td>Stage I-Threshold-90%</td>
    <td>12.0%</td>
    <td>7.32%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Threshold-30%</th>
  </tr>
  <tr>
    <td>Stage I-30% + Stage II-30%</td>
    <td>21.4%</td>
    <td>121.95%</td>
  </tr>
  <tr>
    <td>Stage I-30% + Stage II-40%</td>
    <td>21.8%</td>
    <td>126.83%</td>
  </tr>
  <tr>
    <td>Stage I-30% + Stage II-50%</td>
    <td>19.4%</td>
    <td>97.56%</td>
  </tr>
  <tr>
    <td>Stage I-30% + Stage II-60%</td>
    <td>19.2%</td>
    <td>95.12%</td>
  </tr>
  <tr>
    <td>Stage I-30% + Stage II-70%</td>
    <td>19.0%</td>
    <td>92.68%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Threshold-40%</th>
  </tr>
  <tr>
    <td>Stage I-40% + Stage II-30%</td>
    <td>20.4%</td>
    <td>109.76%</td>
  </tr>
  <tr>
    <td>Stage I-40% + Stage II-40%</td>
    <td>19.8%</td>
    <td>102.44%</td>
  </tr>
  <tr>
    <td>Stage I-40% + Stage II-50%</td>
    <td>17.4%</td>
    <td>73.17%</td>
  </tr>
  <tr>
    <td>Stage I-40% + Stage II-60%</td>
    <td>18.0%</td>
    <td>80.49%</td>
  </tr>
  <tr>
    <th colspan="3">Stage II based on Threshold-50%</th>
  </tr>
  <tr>
    <td>Stage I-50% + Stage II-30%</td>
    <td>20.6%</td>
    <td>112.20%</td>
  </tr>
  <tr>
    <td>Stage I-50% + Stage II-40%</td>
    <td>20.6%</td>
    <td>112.20%</td>
  </tr>
  <tr>
    <td>Stage I-50% + Stage II-50%</td>
    <td>19.4%</td>
    <td>97.56%</td>
  </tr>
  <tr>
    <td>Stage I-50% + Stage II-60%</td>
    <td>18.6%</td>
    <td>87.80%</td>
  </tr>
</table>

Table 11: Deepseek-MATH

Reviewer paper summaries: The paper proposes a weak-to-strong generalization strategy, where a less capable model generates initial labels that are used to fine-tune a stronger model. However, because the weak supervisor’s labels can be noisy, the authors introduce a two-stage framework: the first stage improves the quality of supervision by filtering for high-confidence responses, and the second stage refines question quality by having the already fine-tuned strong model regenerate answers for discarded questions. Experiments across multiple large language model series and mathematical reasoning benchmarks show that this approach can improve the performance gap, with some configurations achieving near-complete recovery of the gap.

This submission proposes a training scheme for improving an LM finetuning/alignment setup that has recently been termed in different papers as "weak-to-strong generalization", "scalable oversight", or "superalignment". The setup involves a -- possibly superhumanly -- "strong" model that we want to finetune/align towards a hard task for which good labels are difficult to obtain, and a "weak" model that can provide noisy labels for this task. Recent work has shown that it is possible to achieve a certain degree of weak-to-strong generalization by using the labels from the weak model to finetune the strong model.
Concretely, the proposed method consists of two steps:
1. Use labels that were predicted by the weak model with high confidence to finetune the strong model
2. Have the strong model strong predict labels, filter out low-confidence labels, add high-confidence labels to the finetuning data and finetune the strong model again

Evaluation of two LMs on two tasks shows strong performance gains.
Summary of strengths: 1- The paper analyzes the impact of filtering thresholds well. This can be useful for future studies and help researchers to have a better idea of how this factor can change the distribution of questions and impact the final performance.

2- The framework is evaluated with multiple state-of-the-art models and different configurations, showing consistent improvements in PGR and task performance.

1. While the general idea of selecting high-confidence labels for iterative refinement is not terribly new and the proposed method can be essentially thought of as sitting somewhere between self-training and co-training, i.e., semi-supervised methods that are well-established in the classical machine learning literature, this particular instantiation in the context of weak-to-strong generalization in the context of LMs appears new to me.
2. The method shows strong empirical performance
3. The paper is written well and is easy to follow
Summary of weaknesses: 1- The authors claim that strong models exhibit significant overfitting in weak-to-strong generalization by citing Yang et al. (2024a). However, Yang et al. (2024a) actually show that strong students tend to produce misaligned outputs in regions where weak teachers have limited coverage, particularly when conflicting alignment objectives exist. Overfitting typically refers to the strong model excessively fitting the noise in the training labels, resulting in deteriorated performance on unseen data. In contrast, the misalignment discussed by Yang et al. (2024a) seems to involve the strong model “deceiving” or deviating from expected behavior in areas that the weak model has not adequately supervised. the paper would benefit from additional evidence that distinguishes between overfitting and misalignment. Providing detailed learning curves or controlled experiments that isolate the effects of overfitting.

2- The paper argues that a two-stage framework—with an initial filtering of weak labels (Stage I) followed by data refinement (Stage II)—is necessary to mitigate overfitting. However, it is unclear whether Stage I is necessary. Recent methods which primarily rely on a strong model to generate and refine its own training data (only executing Stage II), have demonstrated promising results. This raises the question: Do we need a weak model at all in the discussed domain, or can a strong model independently produce a robust A+B training set? The paper would benefit from a direct empirical comparison between the two approaches to justify the necessity of incorporating a weak supervisor in Stage I.

3- The current experiments are mainly on math reasoning tasks. This paper can benefit from experiments on different task types to check the effectiveness of the approach on different test sets. Also, analyzing how setting thresholds on different task types might impact the final performance.

Edit after author response: This point has been addressed.

~~1. The evaluation is quite limited, both in terms of tasks and number of models. I know that it is easy for reviewers to demand more models and more tasks, but this submission makes a purely empirical claim about the superiority of a proposed method, i.e., "our new method gives better performance!", and having two similar (math) tasks is just not enough. The submission mentions limited resources, but reports many experiments analyzing the impact of the thresholding the label noise (appendix p16-19) and I believe spending some of that compute on more diverse tasks and different models would have been a better choice.~~
Comments, suggestions and typos: In the main paper in multiple places have been mentioned that the experiments are mainly on math reasoning tasks, while in the appendix they have briefly mentioned experiments on SciQ which I believe should have been introduced and mentioned in the main paper. Also, the community standard for science evaluation has been MMLU to my knowledge, and adding MMLU would be a better metric compared to SciQ.

none
Overall assessment scores: 3.5

3.5
Metareview score: 4
Metareview: The paper proposes a weak-to-strong generalization approach, where a less capable model is used to generate labels for fine-tuning a stronger model. To address the potential noisiness of the weak model’s outputs, the authors introduce a two-stage filtering framework. In the first stage, high-confidence responses are selected to improve supervision quality. In the second stage, the fine-tuned strong model is used to generate answers for the previously discarded questions. Experiments on two mathematical reasoning datasets and two large language model architectures, demonstrate consistent performance gains.
Summary of reasons to publish: The reviewers highlighted the following strengths:

- The paper is well-written and easy to follow.

- The proposed method demonstrates strong empirical performance. The application of semi-supervised learning in a weak-to-strong generalization setting is novel and interesting.

- The experimental evaluation is comprehensive, using state-of-the-art LLMs. Detailed analysis provides insights that could help guide future research in this area.