Learning from All: Concept Alignment for Autonomous Distillation from Multiple Drifting MLLMs

## Abstract

Abstract This paper identifies a critical yet underexplored challenge in distilling from multi-modal large language models (MLLMs): the reasoning trajectories generated by multiple drifting teachers exhibit concept drift, whereby their reasoning distributions evolve unpredictably and transmit biases to the student model, ultimately compromising its performance.
To tackle this issue, we pioneer a theoretical connection between concept drift and knowledge distillation, casting the non-stationary reasoning dynamics from multiple MLLM teachers as next-token prediction of multi-stream reasoning trajectories.
Guided by concept drift, we introduce the “learn–compare–critique” paradigm, culminating in autonomous preference optimization (APO).
Under the active guidance of the teachers, the student model first learns and self-distils preferred thinking by comparing multiple teachers. It then engages in critical reflection over the drifting inference from teachers, performing concept alignment through APO, ultimately yielding a robust, consistent, and generalizable model.
Extensive experiments demonstrate our superior performance of consistency, robustness and generalization within knowledge distillation.
Besides, we also contributed a large-scale dataset CXR-MAX (Multi-teachers Alignment X-rays), comprising 170,982 distilled reasoning trajectories derived from publicly accessible MLLMs based on MIMIC-CXR. Our code and data are public at: https://anonymous.4open.science/r/Autonomous-Distillation/ .

## 1 Introduction

Knowledge distillation (KD) has emerged as a central paradigm for transferring knowledge from general large-scale teacher models, such as multi-modal large language models (MLLMs), to more compact student models. This is particularly in customized domain-sensitive settings such as the medical field [1, 2, 3, 4, 5, 6]. Recent advances in multi-teacher distillation further highlight the promise of leveraging complementary expertise and diverse domain-specific priors from multiple teacher models to enhance the student’s learning [7, 8]. However, this paradigm remains fundamentally constrained by the non-stationary dynamics of drifting MLLMs. Specifically, the inherent inter-model drift among multiple teachers progressively destabilizes the concept alignment between the student model’s representation space and the real-world domain. This misalignment driven by model drift can lead to catastrophic error propagation, critically threatening the reliability of models in safety-sensitive applications.

Concept drift theory offers a compelling analytical lens to examine concept alignment. It allows us to characterize not only the inter-model drift among MLLM teachers but also the hierarchical drift between the teacher ensemble and the student model under non-stationary custom-tuning [9, 10]. This framework is adept at capturing the unpredictable distributional shifts that unfold across their parallel reasoning trajectories. Within this perspective, the autoregressive decoding paradigm of a single MLLM can be viewed as a sequential token-generation process, where each step propagates through the model’s latent reasoning pathways. Consequently, when multiple teachers are involved, their parallel autoregressive processes are transformed into a multi-stream drift scenario, where each process follows its own distributional dynamics that may drift asynchronously and in heterogeneous directions. These asynchronous drifts, which can manifest as convergence, divergence, or even direct conflict, fundamentally distinguish the student’s learning challenge from the single-teacher setting.

Figure: (a) Concept Drift Among MLLM Teachers
Refer to caption: x1.png

Within the concept drift framework, our analysis uncovers fundamental limitations in distilling knowledge from multiple drifting MLLMs in customized domain-specific scenarios.
Figure [1(a)](https://arxiv.org/html/2510.04142v1#S1.F1.sf1) presents the diagnostic reasoning outcomes of various teacher MLLMs on MIMIC-CXR, featuring seven leading publicly accessible MLLMs with precision, recall, and semantic similarity, where precision and recall specifically refer to the alignment between radiological findings in the MLLM generated reports and in the ground-truth radiology reports written by radiologists, rather than disease-classification accuracy. The limitations of multiple drift MLLMs in domain-specific scenarios are manifested in:

###### Observation 1.1

Notable divergence and complementarity exists among teacher models: while Qwen-VL-Max and Grok-4 achieve high precision but relatively low recall, favoring concise yet accurate feature descriptions. GPT-5 attains the highest recall with less impressive precision, capturing a broader range of imaging features at the cost of introducing potentially redundant details. This highlights that the substantial concept drift inherent among teachers poses a significant challenge for distillation, yet their pronounced complementarity presents a compelling advantage over distillation from any individual teacher.

###### Observation 1.2

Distilled student also inherits distributional biases from drifting MLLM teachers:
In Figure [1(b)](https://arxiv.org/html/2510.04142v1#S1.F1.sf2), the distilled student does not merely inherit the complementary strengths of multiple drifting MLLMs but also internalizes their distributional biases, leading to systematic errors such as overgeneralization, semantic inconsistency, and erroneous rationalization.
As a result, the student becomes a drift-biased amalgamation of its teachers rather than a balanced integrator.

Therefore, summarizing the above challenges of knowledge distillation from multiple drifting MLLMs, it raises the important question:

How to maintain concept alignment in distillation with multiple drifting MLLM teachers?

Inspired by advances in reinforcement learning [11, 12, 13], we propose the autonomous distillation that leverages multiple drifting MLLM teachers to build a generalized, consistent and robust student model under customized specific domain.

It follows the principled “learn–compare–critique” paradigm. First, in the learning stage, the student model absorbs knowledge distilled from multiple teachers, thereby acquiring broad domain-specific expertise while inevitably inheriting certain biases. Then, in the comparison stage, the student performs self-distillation by aligning concepts across the outputs of different teachers, which refines the acquired knowledge into a more consistent representation. Finally, in the criticism stage, we introduce autonomous preference optimization (APO), where the consensus knowledge distilled through self-alignment is treated as a preferred preference, while the biased components of individual teachers are regarded as negative preferences. It leverages the reinforced learning to mitigate biases inherited from drifting teachers in the initial learning stage, enabling effective and efficient preference alignment that enhances robustness and generalization.

In summary, our paper mainly makes the following contributions:

- 1.
We establish a novel theoretical framework that casts the autoregressive generation of reasoning trajectories in MLLMs within the perspective of concept drift theory, providing a principled foundation for understanding and analyzing knowledge distillation from multiple drifting teachers.
- 2.
Second, building upon the concept drift theory, we design an autonomous distillation framework following a “learn–compare–critique” paradigm. The student first absorbs broad domain-specific knowledge from diverse teachers, then conducts self-distillation to align their concepts, and ultimately employs autonomous preference optimization to reconcile biases and reinforce generalization. Unlike traditional knowledge distillation, the drifting teachers here also act as negative signals, which helps approximate and refine the decision space of the student model.
- 3.
Third, we conduct comprehensive empirical evaluations on diverse clinical benchmarks for chest radiographs, including disease classification, diagnostic report generation, and zero-shot generalization. Despite relying on only one-tenth of the distillation data typically required, our method consistently delivers statistically significant gains in robustness, generalization, and accuracy under multiple drifting MLLM teachers. Furthermore, ablation studies substantiate the effectiveness of each component of our framework.
- 4.
As a pioneer contribution to the community, we introduce CXR-MAX (Multi-teahers Alignment X-rays), a large-scale dataset comprising 170,982 distillation results of reasoning trajectories derived from publicly accessible MLLMs based on MIMIC-CXR.

###### Observation 1.1

###### Observation 1.2

## 2 Methodology

Figure: (a) (a) Supervised Pre-Distillation with Concept Drift
Refer to caption: x2.png

### 2.1 Understanding Teacher Divergences with Multi-Stream Concept Drift

In this section, we extend the notion of concept drift to the setting of multi-teacher MLLMs, emphasizing the unpredictable distributional shifts that arise across the reasoning trajectories of different teachers. Prior studies on concept drift predominantly address single-stream inference [11], where an individual MLLM $\pi$ autoregressively generates the token at position $j$ in the reasoning chain by recursively performing on-policy sampling conditioned on the visual input $v$ and textual prompt $l$, such that the partial token sequence $t_{<j}$ of the CoT trajectory is given by

$$ $t_{j}\sim\pi(\cdot\mid v,l,t_{<j}).$ (1) $$

Accordingly, the single-stream reasoning process of an MLLM can be formalized as follows:

###### Definition 2.1

Intuitively, the autoregressive reasoning trajectory of an MLLM unfolds as a chain-of-thought (CoT) stream $S_{0,i}=\{s_{0},\ldots,s_{i}\}$, where each state $s_{j}=(t_{<j},z_{j})$ comprises the partial token sequence $t_{<j}$ generated up to step $j$ together with the corresponding latent predictive distribution $z_{j}$ that governs the subsequent generation.

Capitalizing this formulation, we generalize the concept drift framework from a single MLLM to the multi-teacher setting, where each teacher is associated with a distinct reasoning trajectory. Formally, we define the multi-stream concept drift of the reasoning process as follows:

###### Definition 2.2

Consider $N$ CoT streams corresponding to $N$ MLLM teachers, with their reasoning trajectories denoted by $\mathbb{S}_{0,i}=\{\mathcal{S}_{0},\ldots,\mathcal{S}_{i}\}$, where $\mathcal{S}_{j}=(s^{1}_{j},\ldots,s^{N}_{j})$, and $s^{m}_{j}$ represents the state from the $m$-th teacher. Let $\mathbb{S}_{0,i}$ follow a certain distribution $F_{0,i}(\mathcal{S}{m})$. Concept drift among the various MLLM teachers is said to occur at step $m+1$ if $P_{0,i}(\mathcal{S}_{m})\neq P_{i+1,\infty}(\mathcal{S}_{m})$, which can be expressed as

$$ $\exists t:P_{i}(\mathcal{S}_{m})\neq P_{i+1}(\mathcal{S}_{m}).$ (2) $$

where the teachers’ predictions are conditionally independent given their own prior states, leading the joint probability $P_{i}(\mathcal{S}_{m})$ can be decomposed as

$$ $P_{i}(\mathcal{S}_{m})=P_{i}(t_{<m}^{1},\ldots,t^{N}_{<m})\prod_{u=1}^{N}P_{i}(z_{m}^{u}|t_{<m}^{u})$ (3) $$

Furthermore, we assume that the teachers do not interact during reasoning, so their historical trajectories $t_{<m}$ are statistically independent, allowing the joint distribution to be factorized into the product of the marginal distributions of individual teachers, thereby yielding a fully decomposed form:

$$ $P_{i}(\mathcal{S}_{m})=\prod_{u=1}^{N}P_{i}(t_{<m}^{u})P_{i}(z_{m}^{u}|t_{<m}^{u})$ (4) $$

Therefore, with the notion of multi-stream concept drift of MLLM reasoning trajectories in place, we are able to effectively capture the dynamic variations that emerge during the knowledge distillation from multiple teachers, formalized as the concept drift process $\prod_{u=1}^{N}P_{i}(t_{<m}^{u})$, and its induced probabilistic divergence $\prod_{u=1}^{N}P_{i}(z_{m}^{u}|t_{<m}^{u})$,. Specifically, the framework enables us to quantify the evolving discrepancy between the expected predictive distribution and the realized distribution of cognitive states throughout the reasoning trajectory, which directly governs the alignment of concepts learned by the student.

###### Definition 2.1

###### Definition 2.2

### 2.2 Harmonizing Drifting MLLM Teachers via Concept Alignment

Building on the formalization of concept drift across multiple MLLM teachers in Eq. [4](https://arxiv.org/html/2510.04142v1#S2.E4), we reveal intrinsic inconsistencies and biases in their domain knowledge that, if distilled naively, would inevitably propagate to the student model and manifest as systematic errors, as demonstrated in Observation [1.2](https://arxiv.org/html/2510.04142v1#S1.Thmtheorem2). To address this challenge, we propose an approach that integrates the drifting knowledge of multiple teachers while enforcing concept alignment not only across teachers but also between the teachers and the student model through self-distillation.

First, our model undergoes a supervised pre-distillation phase with multiple drifting MLLM teachers, during which it broadly assimilates their collective predictions despite the presence of concept drift, as illustrated in Fig. [2(a)](https://arxiv.org/html/2510.04142v1#S2.F2.sf1). Specifically, at each reasoning step $m$, the teachers provide a set of predictive distributions $\mathcal{Z}_{m}$ from which the student model absorbs heterogeneous knowledge across distinct reasoning trajectories, formalized as

$$ $\mathcal{Z}_{m}=\{P_{i}(z_{m}^{u}|t^{u}_{<m})\}^{N}_{u=1}$ (5) $$

Thus, in this stage, the goal of the student is to project domain-specific distributions into the representational concept space jointly recognized by the MLLM teachers, as depicted in Fig. [2(b)](https://arxiv.org/html/2510.04142v1#S2.F2.sf2). Formally, the objective can be expressed as

$$ $q^{*}(z_{m}|t_{<m})=\arg\min_{q}\sum_{u=1}^{N}D_{KL}(P_{i}(z_{m}^{u}|t^{u}_{<m})||q(z_{m}|t_{<m}))$ (6) $$

where $q^{*}$ denotes the unified distribution that encapsulates the collective knowledge of all MLLM teachers within the student model. In this context, the student integrates the drifting teacher distributions into a coherent internal representation, reconciling heterogeneous signals not by following any single teacher, but by minimizing their collective divergence from an aligned target distribution $q(z_{m}\mid t_{<m})$.

Consequently, the pre-distilled student model $\hat{\pi}_{st}$ has assimilated the specific-domain knowledge from diverse teachers via pre-distillation, mitigating potential omissions during the reasoning. The subsequent step addresses the drift across teachers by employing self-distillation to compare their underlying concepts and extract the consistent ones. Specifically, We aggregate the CoT sequences $\mathcal{T}=\{t^{1},\ldots,t^{N}\}$ generated by various teachers for the same instance, and condition the sampling process on the concatenated trajectories jointly with the input image $v$ and prompt $l$, thereby deriving self-distilled outcomes that enforce conceptual alignment:

$$ $t^{+}\sim\hat{\pi}_{st}(\cdot|v,l,\mathcal{T})$ (7) $$

At this stage, our framework consolidates insights from multiple drifting MLLM teachers through self-distillation, yielding the preferred reasoning trajectories while distilling the teachers’ feature space into the student.

### 2.3 Aligning Distilled Concepts via Autonomous Preference Optimization

Beyond relying on teachers’ guidance $\mathcal{T}$ to derive preferred reasoning trajectories $t^{+}$ in Eq.[7](https://arxiv.org/html/2510.04142v1#S2.E7), the target student model is expected to autonomously refine its reasoning, toward independent, self-consistent and generalized conclusions. Consequently, following the ”learn-compare-critique” paradigm, the bias reasoning trajectories $\mathcal{T}$ from drifting MLLM teachers serve as negative signals, prompting the student model to critically re-evaluate and refine its acquired knowledge. Through conceptual alignment, the student identifies coherent and rational explanations that resolve inconsistencies, thereby enabling iterative self-improvement and evolutionary learning.

Formally, drawing inspiration from DPO [14], we derive the optimal policy that maximizes the reward function:

$$ $r(v,l,t)=\beta\log\frac{\pi_{\theta}(t|v,l)}{\hat{\pi}_{st}(t|v,l)}$ (8) $$

where $\beta$ is a parameter controlling the deviation from the base reference policy $\hat{\pi}_{\text{st}}$, namely the pre-distilled student model, and $\pi_{\theta}$ denotes the autonomous refining model. With the reward function, we treat the distilled reasoning trajectories $t^{+}$ in Eq.[7](https://arxiv.org/html/2510.04142v1#S2.E7) as the preferred thinking, whereas the raw outputs $\mathcal{T}$ from drifting MLLM teachers are regarded as negative signals that provide biased guidance. Thus, based on the Bradley-Terry model [15], the preferred distilled distribution can be written as:

$$ $P(t^{+}\succ\mathcal{T}|v,l)=\frac{\exp(r(v,l,t^{+}))}{\exp(r(v,l,t^{+}))+\sum_{u=1}^{N}w_{u}\exp(r(v,l,t^{u}))}$ (9) $$

Having expressed the probability of distilled preference reasoning trajectories in terms of the optimal policy, we can now formulate a maximum likelihood objective for the parametrized policy $\pi_{\theta}$, which objective is given by:

$$ $\mathcal{L}_{APO}=-\mathbb{E}_{(v,l,t^{+},\mathcal{T})}\left[\log{P(t^{+}\succ\mathcal{T}|v,l)}\right]$ (10) $$

In this context, combinted with Eq.[8](https://arxiv.org/html/2510.04142v1#S2.E8), Eq.[9](https://arxiv.org/html/2510.04142v1#S2.E9) and Eq.[10](https://arxiv.org/html/2510.04142v1#S2.E10), the antonomous preference optimization (APO) is driving the reinforced distillation with multiple drifting MLLMs teachers through the maximum likelihood objective:

$$ $\mathcal{L}_{APO}=-\mathbb{E}_{(v,l,t^{+},\mathcal{T})}\left[\log\frac{(\frac{\pi_{\theta}(t^{+}|v,l)}{\hat{\pi}_{st}(t^{+}|v,l)})^{\beta}}{(\frac{\pi_{\theta}(t^{+}|v,l)}{\hat{\pi}_{st}(t^{+}|v,l)})^{\beta}+\sum_{u=1}^{N}w_{u}(\frac{\pi_{\theta}(t^{u}|v,l)}{\hat{\pi}_{st}(t^{u}|v,l)})^{\beta}}\right]$ (11) $$

Against this backdrop, by adhering to the paradigm of “learning-compare-critique”, the student model attains concept alignment under the influence of drifting MLLM teachers, thereby mitigating the transmission of biased knowledge. This adaptive evolution not only enhances the model’s internal consistency but also strengthens its generalization and robustness, laying a principled foundation for reliable reasoning across diverse domains.

### 2.4 Building CXR-MAX Dataset for Multi-Teachers Reasoning

Since we are pioneers in introducing the concept drift into the knowledge distillation of multiple MLLMs, we are deeply aware of the scarcity of multiple CoT from various MLLMs in downstream tasks, especially in the highly professional medical field. Consequently, we aim for the model to autonomously adapt to concept drift, selectively assimilating consistent and valuable knowledge from multiple teachers while preventing the inheritance of biases during distillation.

In this context, to rigorously evaluate the potential of a student model trained under multiple drifting teachers, a more realistic training dataset for knowledge distillation is essential. Addressing the need for high-quality chain-of-thought (CoT) data from diverse MLLMs, we introduce CXR-MAX (Multi-teachers Alignment for X-rays), an extension of the MIMIC-CXR dataset [16] incorporating outputs from seven widely used public MLLMs. CXR-MAX provides 170,982 distillation instances of reasoning trajectories covering 14 thoracic pathologies, establishing the first large-scale benchmark for knowledge distillation with multiple MLLM teachers’ reasoning trajectories in clinical chest X-ray interpretation. Additional details are provided in Appendix [C](https://arxiv.org/html/2510.04142v1#A3).

## 3 Experiments

In this section, we verify the robustness, consistency and generalization of our proposed autonomous distillation under multiple drifting teachers.

The MIMIC-CXR dataset [16] serves as an ideal training environment for our method, since medical diagnosis embodies the sophisticated reasoning and high-stakes practicality that our distillation approach aims to capture. It presents 371,920 chest X-rays associated with 227,943 imaging studies from 65,079 patients. And images are provided with 14 labels with corresponding free-text radiology reports, namely Atelectasis (Ate.), Cardiomegaly (Car.), Consolidation (Con.), Edema (Ede.), Enlarged Cardiomediastinum (ECM), Fracture (Fra.), Lung Lesion (LL), Lung Opacity (LO), Pleural Effusion (PE), Pneumonia (Pna.), Pneumothorax (Pnx.), Pleural Other (PO), Support Devices (SD) and No Finding (NF).

Acknowledging the additional computational overhead and costs associated with employing multiple teachers, we intentionally and deliberately restricted our distillation to only 1/10 of the whole MIMIC-CXR, underscoring the efficacy of our method in achieving high-quality knowledge transfer from the drifting teachers, even under limited data conditions.

**Table 1: Evaluation results of multi-label chest diseases classification on MS-CXR-T. Top-1 accuracy is applied to evaluate the performance of different methods. The best-performing models are highlighted in red, with the second-best in blue. Comparison methods include CTrans [17], CheXRelNet [18], BioViL [19] , BioViL-T [17] , Med-ST [20] ,TempA-VLP [21] and CoCa-CXR [22].**
|  | Venue | Con. | PE | Pna. | Pnx. | Ede. | Avg. |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Full Data Training |  |  |  |  |  |  |  |
| CTrans | CVPR’23 | 0.44 | 0.61 | 0.45 | 0.32 | 0.66 | 0.49 |
| CheXRelNet | MICCAI’22 | 0.47 | 0.47 | 0.47 | 0.36 | 0.49 | 0.45 |
| BioViL | ECCV’22 | 0.56 | 0.63 | 0.60 | 0.43 | 0.68 | 0.58 |
| BioViL-T | CVPR’23 | 0.61 | 0.67 | 0.62 | 0.43 | 0.69 | 0.60 |
| Med-ST | ICML’24 | 0.61 | 0.67 | 0.59 | 0.65 | 0.54 | 0.61 |
| TempA-VLP | WACV’25 | 0.65 | 0.59 | 0.73 | 0.43 | 0.77 | 0.64 |
| CoCa-CXR | Arxiv’25 | 0.70 | 0.70 | 0.61 | 0.73 | 0.72 | 0.69 |
| 10% Data Training w/o Radiologist Reports |  |  |  |  |  |  |  |
| Ours | This paper | 0.84 | 0.67 | 0.78 | 0.96 | 0.65 | 0.78 |

Additionally, we relied solely on the classification labels from MIMIC-CXR and did not utilize the original radiology reports for training. It is motivated by our focus on knowledge transfer from MLLMs as teachers, as well as the limited generalizability of human-annotated reports with reasoning trajectories, which are often scarce in the domain-specific area.

In terms of the model, we employ Qwen2.5-VL (7B) [23] as the student model to perform supervised pre-distillation and autonomous preference optimization, cascadedly. And they only train one epoch for each stage with a batch size of 2. More detailed experimental implementations are given in Appendix [D](https://arxiv.org/html/2510.04142v1#A4).

**Table 2: Evaluation results of multiple MLLM teachers on classification of MS-CXR-T for comparison. Top-1 accuracy is applied to evaluate the performance of different methods. The best-performing models are highlighted in red, with the second-best in blue. The comparison MLLMs includes: Claude Sonnet-4[24], Gemini-2.5 [25], GLM-4.5V [26], GPT-5[27], Qwen-VL-Max [23], Grok-4 [28] and Moonshot-v1 [29].**
| MLLMs | Con. | PE | Pna. | Pnx. | Ede. | Avg. |
| --- | --- | --- | --- | --- | --- | --- |
| MLLM Teachers with Huge Parameters |  |  |  |  |  |  |
| GPT-5 | 0.75 | 0.68 | 0.89 | 0.90 | 0.52 | 0.75 |
| Gemini-2.5 | 0.28 | 0.61 | 0.40 | 0.94 | 0.42 | 0.53 |
| Sonnet-4 | 0.89 | 0.69 | 0.48 | 0.15 | 0.15 | 0.47 |
| Qwen-VL-Max | 0.54 | 0.65 | 0.40 | 0.95 | 0.64 | 0.64 |
| Grok-4 | 0.43 | 0.41 | 0.36 | 0.97 | 0.61 | 0.56 |
| GLM-4.5V | 0.59 | 0.67 | 0.52 | 0.96 | 0.72 | 0.69 |
| Moonshot | 0.13 | 0.46 | 0.77 | 0.88 | 0.19 | 0.48 |
| Student Model (7B MLLM) |  |  |  |  |  |  |
| Ours | 0.84 | 0.67 | 0.78 | 0.96 | 0.65 | 0.78 |

### 3.1 Orchestrating Multiple Drifting Teachers for Robust MLLM Distillation

First, to explicitly demonstrate the superior performance of our proposed method under non-stationary environments, especially in robustness, we compare it with other models on MS-CXR-T [30], where instances are chosen from the public MIMIC-CXR.
As presented in Table [1](https://arxiv.org/html/2510.04142v1#S3.T1), our autonomous distillation approach attains a superior overall performance of 0.76, outperforming the second-best method, CoCa-CXR [22] by nearly 13%, especially for only 1/10 of the data without radiologist reports. This result underscores the robustness of our framework under instruction from multiple drifting MLLMs.
While our method trails the top-performing CoCa-CXR by a narrow margin of 0.03 on pleural effusion (PE), we attribute this performance gap to CoCa-CXR’s use of additional data from Chest ImaGenome [31] in addition to standard MIMIC-CXR.
In terms of the edema (Ede.), we argue that excessive disagreement among the teachers is beyond a reconcilable threshold, as indicated in Table [2](https://arxiv.org/html/2510.04142v1#S3.T2) with accuracy 0.15 of Sonnet-4 [24] and 0.19 of Moonshot [29] on the edema. It severely hinders the student model’s ability to learn a consistent decision policy. Nonetheless, the distilled student model still outperforms the majority of the teacher models on the edema (Ede.) as illustrated in Table [2](https://arxiv.org/html/2510.04142v1#S3.T2).

Beyond comparison with domain-specific methods, we also focus on the robust alignment between drifting MLLM teachers and our distilled student model, as exhibited in Fig.[2](https://arxiv.org/html/2510.04142v1#S3.T2). The compared MLLM teachers all have huge parameters, while our distilled student is a ”small” MLLM with only 7B parameters. Despite it, the distilled student achieves the best average performance with a Top-1 accuracy of 0.78 across all diseases, surpassing every single teacher.
It demonstrates that our autonomous distillation empowers the student model to integrate the diverse strengths of multiple teachers, thereby truly learning from all.

Moreover, as shown in Table [2](https://arxiv.org/html/2510.04142v1#S3.T2), although the distilled student does not surpass any single teacher MLLM on individual diseases, it consistently achieves the second-best performance across all categories except pleural effusion (PE), underscoring its robustness and stability. Notably, for cases where teacher disagreements are particularly pronounced, such as consolidation (Con.) with an accuracy gap of 0.62 and edema (Ede.) with a gap of 0.57, our approach effectively mitigates unpredictable drifts among MLLM teachers, preventing biased knowledge from infiltrating the student. Even in the extreme case of Pneumonia (Pna.), where only a few positive teachers with accuracy above 0.5 (GPT-5, GLM-4.5V, and Moonshot) are overwhelmed by a larger number of negative teachers, the student is still able to leverage APO-based critique to align more closely with positive feedback despite the prevalence of negative signals.

In addition, the teacher comparison further highlights the substantial drifts among multiple MLLMs, which are manifested not only in the absence of a single teacher that consistently performs best across most diseases, but also in the strikingly large accuracy gaps observed among teachers on individual diseases. These findings underscore the considerable challenges posed by multi-teacher distillation and further validate the central Observation [1.1](https://arxiv.org/html/2510.04142v1#S1.Thmtheorem1) of our study.

### 3.2 Harmonizing Concept Alignment for Consistent Thinking

**Table 3: Evaluation results of diagnostic report generation on MIMIC-CXR with various metrics including BLEU-1/-2/-3/-4, ROUGE-L, METEOR and CIDEr. The best-performing models are highlighted in red. The comparison methods include: METransformer [32], R2GenGPT [33],BtspLLM [34] and MambaXray [35]**
|  | Venue | BLEU-1 | BLEU-2 | BLEU-3 | BLEU-4 | ROUGE-L | METEOR |
| --- | --- | --- | --- | --- | --- | --- | --- |
| METransformer | CVPR’23 | 0.386 | 0.250 | 0.169 | 0.124 | 0.291 | 0.152 |
| R2GenGPT | MetaRad’23 | 0.408 | 0.256 | 0.174 | 0.125 | 0.285 | 0.167 |
| PromptMRG | AAAI’24 | 0.398 | - | - | 0.112 | 0.268 | 0.157 |
| BtspLLM | AAAI’24 | 0.402 | 0.262 | 0.180 | 0.128 | 0.291 | 0.175 |
| MambaXray | Arxiv’24 | 0.422 | 0.268 | 0.184 | 0.133 | 0.289 | 0.167 |
| Ours | This paper | 0.463 | 0.272 | 0.210 | 0.152 | 0.298 | 0.213 |

Beyond classification, we further substantiate our core contribution of consistent reasoning, which retains the beneficial CoT across multiple MLLM teachers while suppressing harmful drift. As shown in Table [3](https://arxiv.org/html/2510.04142v1#S3.T3), we assess diagnostic report generation on MIMIC-CXR to evaluate the reasoning capabilities of the distilled student model. Evaluation employs BLEU to quantify terminology precision and reasoning coherence, ROUGE-L to assess narrative completeness, and METEOR to capture synonym-aware lexical alignment.

The results demonstrate that our reasoning framework consistently excels across all evaluation metrics, achieving notable gains in BLEU-4 (14.3%), ROUGE-L (2.4%), and METEOR (27.5%), reflecting the consistency and completeness.
We attribute this improvement to the “critique” phase within autonomous preference optimization. Unlike conventional knowledge distillation, where teachers primarily provide positive guidance, we reinterpret the drifting outputs of teachers as negative samples, while treating the student’s self-distilled, conceptually aligned signals as positive samples for preference learning. This strategy sharpens the decision boundary of the student model, effectively suppresses the transmission of biased information from teachers, and reinforces conceptual consistency within the student.

**Table 4: Evaluation results of zero-shot diseases classification on Open-I[36], ChestXray14 [37], ChestXpert [38] and ChestXDet10 [39]. AUC is applied to evaluate the performance of different methods. The best-performing models are highlighted in red. The comparison methods include: BioViL [17], CheXzero [40], MedKLIP [41], KAD [42] and CARZero [43]**
| Method | Open-I | C’Xray14 | C’Xpert | C’XDet10 |
| --- | --- | --- | --- | --- |
| BioViL | 0.70 | 0.73 | 0.79 | 0.71 |
| CheXzero | 0.76 | 0.73 | 0.88 | 0.71 |
| MedKLIP | 0.76 | 0.73 | 0.88 | 0.71 |
| KAD | 0.81 | 0.79 | 0.91 | 0.74 |
| CARZero | 0.84 | 0.81 | 0.92 | 0.80 |
| Ours | 0.85 | 0.83 | 0.92 | 0.81 |

### 3.3 Amplifying Autonomous Distillation for Generalized Reasoning

Furthermore, we assess the generalization ability of our model on downstream tasks through zero-shot multi-label classification across four diverse benchmarks, as reported in Table [5](https://arxiv.org/html/2510.04142v1#S3.T5). The results show that our APO-driven MLLMs consistently surpass the second-best baseline, CARZero [43], across all datasets, highlighting their robustness and strong generalization even when trained under drifting MLLM teachers.

### 3.4 Ablation Studies

Moreover, we conduct ablation experiments on MIMIC-CXR to validate the feasibility and coordination of the multiple teachers (MT) and autonomous preference optimization (APO) under non-stationary knowledge distillation, as presented in Table [5](https://arxiv.org/html/2510.04142v1#S3.T5).
For the distillation within a single teacher, GPT-5 serves as the teacher due to the best average accuracy among various teachers as exhibited in [2](https://arxiv.org/html/2510.04142v1#S3.T2).
Moreover, since APO inherently relies on concept alignment across multiple teachers, we do not conduct an ablation study of APO under the setting where MT is absent.

The ablation on MT reveals only marginal overall gains, while performance on most diseases, including Con., PE, Pna. and Pnx. deteriorates. This corroborates our observation that the unpredictable drift among teachers severely disrupts the student’s learning and degrades its effectiveness.
Besides, compared with MT and SPD, APO delivers significant accuracy gains across all diseases by blocking the transmission of concept drift and enabling the student to constructively learn all teachers. Thus, the consistent, robust, and generalizable improvements confirm that the performance boost arises from APO itself rather than MT.

## 4 Conclusions And Limitations

In this paper, we introduce autonomous preference optimization (APO), a novel and robust paradigm for generalized knowledge distillation across multiple drifting MLLMs. Grounded in concept drift theory, APO systematically formalizes the biases inherent in the generation of MLLMs and leverages a “learn–compare–critique” paradigm to guide distillation. Through this framework, APO effectively prevents the propagation of concept drift while enabling the student model to learn from all teachers, assimilating the complementary strengths in a constructive manner.

We envision that this work will stimulate further progress in knowledge distillation for MLLMs, particularly in addressing domain-specific biases. Looking ahead, our future efforts will concentrate on enhancing the efficiency and reducing the computational cost of distillation in large-scale multimodal settings.

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## Appendix A Appendix

## Appendix B Related Works

### B.1 Concept Drift

Building on an extensive body of work, Lu et al. [9, 44] provide a systematic survey that organizes concept drift mitigation into three dominant families: error rate–driven adaptation [45, 46], distribution-aware approaches [10, 47, 48, 49, 50, 51], and multi-hypothesis frameworks [52, 53, 54]. Our study is situated within the distribution-oriented stream, which is notable for coupling rigorous statistical tests with broad representational power, thereby enabling not only accurate detection of drift but also its nuanced characterization along temporal, spatial, and quantitative axes. By supporting fine-grained diagnostics such as the timing of drift onset, the attribution of drift to specific feature subspaces, and the assessment of its magnitude, distribution-based methods provide a principled foundation for adaptive systems that demand both interpretability and precise recalibration in the presence of evolving data.

Ongoing research on concept drift adaptation has produced a wide spectrum of refined techniques designed for increasingly complex learning environments [55]. Among them, the Online Boosting Adaptive Learning (OBAL) framework [52] offers a two-stage pipeline for multistream classification, beginning with Adaptive Covariate Shift Adaptation (AdaCOSA) to capture evolving inter-stream correlations, and subsequently employing a Gaussian Mixture Model–driven weighting scheme to counter asynchronous distributional changes. In the multimodal landscape, CDMLLM [10, 56] highlights the susceptibility of vision–language models to drift-induced biases that arise during both pre-training and fine-tuning, and proposes a unified remedy that integrates T-distribution calibration for long-tailed scenarios with explicit out-of-distribution detection, thereby reinforcing alignment stability. Beyond single-stream settings, GDDM [57] contributes a distribution-free statistical mechanism for uncovering subtle group-level shifts in multi-stream data, relying on adaptive hypothesis testing to achieve robust detection. Anticipatory strategies have also been explored, most notably in DDG-DA [58], which projects potential environmental evolution by coupling predictive factor analysis with synthetic data generation, creating a principled bridge between current observations and future distributional states. Complementing these supervised paradigms, STUDD [48] introduces an unsupervised teacher–student discrepancy model that measures predictive consistency to flag drift without dependence on annotated labels, thereby reconciling sensitivity to distributional change with the practical limitations of real-world deployment.

### B.2 Knowledge Distillation for LLMs and MLLMs

Knowledge distillation (KD) [59] has become a central paradigm for transferring knowledge from large teachers to compact students.
In NLP, KD was initially used to compress BERT-like models [60, 61, 62], enabling efficiency without major performance loss.
More recently, KD has been extended to large language models (LLMs), where the focus is not only on compression but also on improving reasoning robustness, alignment, and sample efficiency.
For example, self-distillation and on-policy KD [63, 64] help students learn from their own generated trajectories or mitigate distribution gaps during fine-tuning.
Other works integrate distillation with instruction tuning [65, 66], preference optimization [67, 68], or symbolic reasoning [69, 70], highlighting the versatility of KD for enhancing LLM performance.

In multi-modal contexts, KD plays an essential role in bridging cross-modal representations.
Vision-language models such as CLIP [71] and FILIP [72] motivated distillation strategies for multi-modal grounding.
Frameworks like BLIP-2 [73], LLaVA [74], InstructBLIP [75], and LLaVA-MoD [76] employ KD from strong encoders or larger MLLMs to align modalities, compress architectures, or improve downstream reasoning.
Recent innovations include Align-KD [77] and MoVE-KD [78], which distill cross-modal alignment knowledge and ensemble signals from multiple visual encoders, respectively, demonstrating the growing interest in efficient and robust MLLM distillation.
KD has also been explored for domain-specific applications, such as robotic surgery VQA [79] and medical multimodal models [80, 81, 82, 83, 84].

A complementary direction is multi-teacher distillation, which synthesizes complementary strengths from multiple teachers.
Early studies in CV [85, 86, 87, 88, 89] inspired recent extensions to LLMs and MLLMs.
For example, Gu et al. [90] propose multi-MLLM distillation for out-of-context news detection, while multi-teacher continual learning frameworks [79] address streaming data in specialized domains.
Moreover, recent benchmarks on distillation under distribution shift [91] highlight the challenge of biased or drifting teacher supervision, which has not been systematically solved in multi-modal KD.

Overall, KD has evolved from compression to a general tool for alignment, reasoning transfer, and cross-modal adaptation.
Yet, most methods remain constrained by single-teacher assumptions or overlook distribution drift across modalities.
These gaps motivate our work on robust multi-teacher distillation for MLLMs, explicitly addressing the challenges of bias inheritance and teacher drift.

### B.3 Reinforced Fine-tuning in LLMs

The role of reinforcement learning (RL) in shaping post-training alignment of large language models (LLMs) has advanced significantly since OpenAI’s pioneering work on Reinforcement Learning from Human Feedback (RLHF) [92], which introduced a paradigm for aligning model behavior with human values [67]. Initial implementations, such as OpenAI-o1 [93], demonstrated the practical utility of preference-driven modeling, yet the reliance on large-scale human annotation quickly revealed severe limitations in cost and scalability. These constraints have spurred a transition toward automated reward construction using pre-trained systems, opening the door to a new generation of alignment methods. Bai et al.’s [68] constitutional framework, for example, relies on sparse natural language feedback as an indirect supervisory signal, while DeepSeek’s research line illustrates a staged trajectory: beginning with a purely RL-based baseline (R0), and subsequently extending to the R1 system [94], which cycles between supervised fine-tuning and their GRPO optimization scheme [95]. This cyclic design improved generalization capacity and marks a broader trend toward increasingly autonomous alignment pipelines that minimize human involvement while retaining robust performance.

Concurrently, alignment research has diversified through a range of novel paradigms that extend beyond the classical RLHF formulation. ReST [96] advances iterative self-training by generating policy-driven samples and refining them via offline RL, while DPO [14] reconceptualizes the task as direct optimization of preferences through implicit reward modeling. Complementary efforts include Rejection Sampling Fine-Tuning (RSFT) [97], which augments supervised training with carefully filtered reasoning trajectories, and ReFT [98], which couples supervised fine-tuning initialization with PPO-based exploration to progressively expand reasoning capabilities. Extending these principles to multimodal contexts, Visual-RFT [99] adapts GRPO-driven strategies for visual-language alignment under limited data regimes, whereas B-STaR [100] introduces dynamic configuration mechanisms that balance exploration and exploitation for self-improving systems. Methodological innovation has also been paralleled by advances in evaluation: Qwen-Math-PRM [101] integrates Monte Carlo estimation with LLM-as-judge consensus, building a hierarchical framework that captures both stepwise reasoning fidelity and holistic solution quality. Along a similar line, ViLaM [102, 6] leverages reinforcement learning for unsupervised visual grounding, tackling the challenges of open-world multimodal understanding.

## Appendix C CXR-MAX Dataset

In this section, we showcase the samples utilized for training and validation in our study, generated by various MLLMs, with the image and ground truth of the radiology report. And the prompt we used to various MLLMs is:

””This is a patient’s chest DR image. The patient has been diagnosed with ¡diseases¿. Can you find the basis for the diagnosis in the image?”

[Uncaptioned image]: images/c6aa502d-e6e8b118-f70e741d-b28dfc45-c73c705d.jpg

## Appendix D Implementation Details

In this section, implementation details are provided.

In terms of the supervised fine-tuning progress, the hyperparameters are presented in Table [6](https://arxiv.org/html/2510.04142v1#A4.T6). Qwen2.5-VL (7B) [23] is applied as our pre-trained model. During the SPD, we utilize the AdamW optimizer, which is configured with a cosine annealing schedule as the learning policy. The initial learning rate is set to $1\times 10^{-4}$, and the AdamW optimizer is employed with hyperparameters $\beta=(0.9,0.98)$. Additionally, we set the weight decay to 0.05 and the dropout rate to 0.1. During the first 20 warm-up steps, the learning rate increases to $1\times 10^{-4}$, and subsequently decays to $10^{-7}$. Unless otherwise specified, the supervised pre-distillation of our multi-modal large language model consists of 10,686 steps, executed on $2\times 2$ NVIDIA A100 GPUs.

**Table 6: The training hyperparameters of our MLLM.**
| Supervised Pre-Distillation |  |
| --- | --- |
| Training Steps | 10,686 |
| Warmup Steps | 20 |
| Warmup Ratio | 0.05 |
| Optimizer | AdamW |
| Learning Rate | 1e-4 |
| Learning Rate Decay | Cosine |
| Adam $\beta$ | (0.9, 0.98) |
| Weight Decay | 0.05 |
| Batch Size | 2 |

While in the autonomous preference optimization (APO), the initial learning rate is reduced to $2\times 10^{-5}$ without the warmup, with the batch size of 2. The visual encoder and text decoder are frozen out of the training. The reinforced custom-tuning consists of 12,132 steps, executed on $2\times 2$ NVIDIA A100 GPUs. Other training parameters are the same as the fine-tuning.

## Appendix E More Results

**Table 7: Evaluation results of diagnostic report generation on MIMIC-CXR with various metrics including BLEU-1/-2/-3/-4, ROUGE-L, METEOR and CIDEr. The best-performing models are highlighted in red. The comparison methods include: R2Gen [103], PPKED [104], AlignTrans [105], CMCL [106], Clinical-BERT [107] ,METransformer [32] ,DCL [108], R2GenGPT [33] ,PromptMRG [109] ,BtspLLM [34] ,MambaXray [35], CounterfactRFT [11]**
|  | Venue | BLEU-1 | BLEU-2 | BLEU-3 | BLEU-4 | ROUGE-L | METEOR |
| --- | --- | --- | --- | --- | --- | --- | --- |
| METransformer | CVPR’23 | 0.386 | 0.250 | 0.169 | 0.124 | 0.291 | 0.152 |
| R2GenGPT | MetaRad’23 | 0.408 | 0.256 | 0.174 | 0.125 | 0.285 | 0.167 |
| BtspLLM | AAAI’24 | 0.402 | 0.262 | 0.180 | 0.128 | 0.291 | 0.175 |
| MambaXray | Arxiv’24 | 0.422 | 0.268 | 0.184 | 0.133 | 0.289 | 0.167 |
| CounterfactRFT | NeurIPS’25 | 0.426 | 0.288 | 0.186 | 0.155 | 0.421 | 0.286 |
| Ours | This paper | 0.563 | 0.372 | 0.270 | 0.192 | 0.298 | 0.213 |

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 2
presentation: 1
questions: In addition to the issues mentioned in the Weaknesses, I have a few more concerns:

1. Although the authors mention high computational cost, the paper lacks concrete efficiency analysis or runtime comparison experiments.
2. Is it always better to use more and stronger teacher models?
soundness: 2
strengths: 1. The paper establishes a clear theoretical link between concept drift and knowledge distillation, extending the KD paradigm to multi-teacher and non-stationary environments.
2. The introduction of the CXR-MAX dataset provides a new and valuable benchmark for the medical imaging community.
summary: This paper addresses the underexplored problem of knowledge distillation from multiple drifting MLLMs, where inconsistent reasoning trajectories across teachers cause concept drift and bias propagation. The authors propose APO, a “learn–compare–critique” paradigm that enables the student model to self-distill and align reasoning concepts autonomously. Experiments show that this method has certain effectiveness.
weaknesses: 1. The paper’s motivation is unclear. Although the authors claim to study the distillation of MLLMs, they do not compare the method with other mainstream methods[1,2,3,4,5,6] in the distillation community. In my view, this work reads more like a medical image understanding paper rather than a genuine distillation study.
2. All experiments are conducted solely on the CXR-MAX dataset, making it difficult to demonstrate the general effectiveness of the proposed method.
3. The assumptions are overly strong. In Equation (4), the authors assume that the inference trajectories of teacher models are independent. This assumption is unrealistic in practice since different MLLMs often originate from similar pretraining distributions.
4. The paper claims theoretical contributions, but the proposed APO objective is mainly a reformulation of DPO and lacks a theoretical analysis of convergence and stability under multi-teacher drift scenarios.
5. The notation in the theoretical section is confusing, with many symbols left undefined or unexplained, making the derivations hard to follow.
6. The font size in Figure 2 is too small, making it difficult to read.

[1] f-Divergence Minimization for Sequence-Level Knowledge Distillation. ACL 2023.

[2] DistiLLM: Towards Streamlined Distillation for Large Language Models. ICML 2024.

[3] MiniLLM: Knowledge Distillation of Large Language Models. ICLR 2024.

[4] Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models. COLING 2025.

[5] ABKD: Pursuing a Proper Allocation of the Probability Mass in Knowledge Distillation via alpha-beta-Divergence. ICML 2025.

[6] DA-KD: Difficulty-Aware Knowledge Distillation for Efficient Large Language Models. ICML 2025.

---

confidence: 3
contribution: 2
initial_rating: 2
presentation: 3
questions: Same as weakness.
soundness: 2
strengths: - The paper is well-written and easy to understand. The concept drift problem is clear and easy to follow.
- The core idea is straightforward: using the student policy model to rephrase the distilled multiple teacher model responses and then learning from it, whether using imitation learning (SFT) or using offline RL (DPO), which is used in this work.
summary: - This paper aims to address the challenge of knowledge distillation from multiple, heterogeneous MLLMs. The main challenge is the concept drift problem, where the teacher models provide conflicting information that can confuse the student model.
- To tackle this, this work proposes a novel three-stage "learn-compare-critique" paradigm called Autonomous Preference Optimization (APO). The student model first learn a broad knowledge via standard supervised distillation from all teachers. Second, it compares and aggregates the teachers' outputs and performs self-distillation to generate a unified reasoning trajectory. Finally, it critiques the initial knowledge by using the consensus trajectory as a preferred sample and the individual teacher outputs as negative samples using a simple contrastive learning loss.
weaknesses: - How the model actually creates the consensus answer from all the different teacher outputs. Is the text from all teachers combined?  If so, how to do this when the multiple teacher outputs are too long for the student model? 
- The self-distillation process is a critical step in this work. However, the student model does not undergo specific training on the aggregation task of multiple teacher outputs; how can it generate positive samples for subsequent DPO training?
- The APO framework treats the outputs from all strong teacher models (e.g., GPT-5) as negative samples, which assumes that the pre-distilled small student model can provide better responses based on the aggregation outputs. This claim should have a solid experiment to verify (e.g., using a rule/model-based score to justify this). 
- This work lacks a lot of baselines to be compared with in the main results: (1) single teacher distillation (KL-based / response-based); (2) naive multi-teacher distillation (KL-based / response-based); (3) distill-then-merge methods.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: How sensitive is APO to the number or quality of teacher models? Would it still help if one teacher is significantly weaker?
soundness: 3
strengths: 1. The paper studies an interesting and practical problem of distilling knowledge from multiple MLLMs, and the identified challenge of concept drift across models or over time is reasonable and well motivated.

2. The proposed “learn–compare–critique” pipeline is conceptually sound and clearly presented.

3. The experimental results are promising, showing that the proposed approach outperforms both previous distillation methods and the original teacher MLLMs. The ablation study provides useful evidence on the contribution of each component and helps analyze the effectiveness of the proposed framework.
summary: The paper studies the problem of knowledge distillation from multiple multimodal large language models (MLLMs). The authors observe that the reasoning trajectories of different teacher models can change inconsistently across models or over time, and that such concept drift can propagate to student models during distillation. To address this issue, the paper proposes a “learn–compare–critique” pipeline. The student model first learns from multiple MLLM teachers; then it performs self-distillation to align and identify inconsistent teacher outputs. Finally, through a preference optimization step, the student reinforces alignment with stable reasoning outputs while down-weighting drifted or biased outputs.

For experiments, the authors construct the CXR-MAX dataset, which is an extension of the MIMIC-CXR dataset by adding reasoning trajectories about clinical chest X-ray interpretation from multiple MLLM teachers. Results show that the proposed method outperforms other existing distillation methods, while achieving performance comparable to or exceeding that of the original teacher models.
weaknesses: 1. All experiments are conducted in the medical imaging domain, specifically chest X-ray reasoning. The paper does not provide evidence that the proposed approach generalizes to other modalities (e.g., vision–language reasoning, VQA) or non-medical tasks, which limits its demonstrated scope.

2. The study uses only one base model (Qwen2.5-VL, 7B) as the student backbone throughout experiments. It remains uncertain whether the proposed framework would behave similarly to other models or model scales.

3. The paper’s citation style does not follow standard formatting, and some theoretical sections (e.g., formal definitions of concept drift) are expressed in dense, overly complex language. Simplifying these formulations and improving readability would make the paper more accessible.

4. The paper could be strengthened by discussing the method's limitations.

---

confidence: 2
contribution: 2
initial_rating: 4
presentation: 2
questions: 1. How does $t^{+}$ generated exactly in Eq (7)? Does it aggregate the results of $\mathcal{T}$? If it's the response from $\pi_{st} $, given input image $v$ and prompt $l$, then it should be $t^+ \sim \pi_{st} (\cdot|v,l)$. 

2. Experiments are mainly done on the domain of Chest X-ray. What would the performance of APO be on other general domains of visual reasoning?

3. Is APO sensitive to the choice of teacher models?
soundness: 2
strengths: 1. The paper is well structured
2. Extensive experiments demonstrate the effectiveness of the proposed method.
summary: This paper discusses the concept drift problem in knowledge distillation of multimodal large language models (MLLM). Through the analysis of the connection between concept drift and knowledge distillation, the authors introduce the “learn–compare–critique” paradigm to tackle the issue. The resulting method, autonomous preference optimization (APO), trains the student with self relection over the drifting inference for concept alignment. Experiments demonstrate the effectiveness of APO on knowledge distillation tasks. The authors also contribute to a large-scale dataset called CXR-MAX.
weaknesses: 1. **Unclear notation**: Definition 2.1 and 2.2 are not clear to me. In particular, the definition of $P_{i, j}$ and $P_i$ are not explained anywhere, do they refer to the joint probability distribution across teachers? More broadly, the jump from Eq. (1) to Definition 2.1 and then to multi-stream drift in Definition 2.2 is not intuitive.

2. **Unclear connection between concept drift and MLLM**: the problem of concept drift might occur naturally in multi-agent distillation. And it seems that there is not special connection to "multimodality"

3. The proposed algorithm in Section 2.3 is a natural extension of DPO, which seems not relevant to the definition in section 2.1

4. The figures in the paper contain a lot of annotations, but a more clear pipeline would help readers' understanding.

5. The experiments are entirely focused on chest X-rays.

---

confidence: 3
contribution: 1
initial_rating: 2
presentation: 1
questions: See weaknesses
soundness: 1
strengths: - Conceptually interesting framework that reframes multiple teacher KD as a PO problem over reasoning trajectories and models a *drifting* behavior, although this drifting is not well elaborated.
- The paper provides a clear experimental setup, with the introduction of CSR-MAX dataset to evaluate alignment in medical reasoning.
summary: This paper introduces APO, a new framework for distilling reasoning capabilities from multiple MLLMs that exhibit conceptual drift, defined as variability in their reasoning behaviors or conclusions. The core idea is that APO aggregates all available reasoning trajectories and learns to prefer the self-distillation as positive signals against all negative trajectories. This approach treats distillation as a preference optimization problem, aligning the student model’s reasoning trajectory with the highest-quality outputs among drifting teachers, in a “learn-compare-critique” paradigm. The method is tested on a newly constructed dataset, CXR-MAX, based on chest X-ray interpretation, and shows improvements in accuracy.
weaknesses: There are several weaknesses regarding the lack of clarity in methods, the justification on the problem setting, and the rationale about the limited application of the proposed method.

- The paper is using these kind of terms: “drifting teachers”, “drifting MLLMs”, “inter-model drift”, “concept drift”, but the central concept of “drift” is not well-defined or formalized. A formal mathematical definition of drifting or empirical characterization of what and how constitutes “drift” would help.
- Example in Figure 1 (b) just looks like a standard error or hallucination commonly observed in LLMs. It fails to provide evidence of concept drift, or that this error came from the drifting teachers.
- The paper assumes a multi-teacher distillation setup which is a very specific framework. It is unclear why this setup is necessary when multiple MLLM teachers exhibit inter-model drift. In practice, single-teacher KD is used in general, and the proposed scenario of drifting across many MLLMs is not well justified.
- The authors claim that distilled students exhibit concept drift after learning from misaligned teachers, yet APO selects the student’s own trajectory as the preferred one during the optimization. If the student is already drifting, why is its reasoning preferred?

Experimental demonstration is only limited to chest X-ray image domain, which is very specific and narrow. 

- Since APO is presented as a general framework for MLLM distillation, experiments on broader tasks (e.g., general image classification, natural language reasoning, or multi-step math and coding problems) would provide more compelling evidence of its utility. I cannot find the reason why the authors have applied it exclusively on the medical domain.
- No quantitative evaluation is provided on the degree of concept drift or how much APO reduces this drift. Some metric to evaluate teacher-teacher drifting and teacher-student alignment over time or across tasks would strengthen the claims.


### Writing and Attribution Concerns

There are notable similarities in structure between this paper and prior work, particularly Yang et al. (2025b), which has been cited in this paper. [Walking the Tightrope: Disentangling Beneficial and Detrimental Drifts in Non-Stationary Custom-Tuning, Yang et al., 2025]

For example, the first contribution point in Introduction says:

> We establish a novel theoretical framework that casts the autoregressive generation of reasoning trajectories in MLLMs within the perspective of concept drift theory, providing a principled foundation for understanding and analyzing knowledge distillation from multiple drifting teachers.

**Yang et al. (2025b)**:

> we establish a novel theoretical framework that formalizes autoregressive token generation in MLLMs through the lens of concept drift theory, enabling systematic identification and causal analysis of detrimental reasoning divergence during non-stationary reinforced custom-tuning.

The last contribution point in Introduction says:

> As a pioneer contribution to the community, we introduce CXR-MAX (Multi-teahers Alignment X-rays), a large-scale dataset comprising 170,982 distillation results of reasoning trajectories derived from publicly accessible MLLMs based on MIMIC-CXR.

**Yang et al. (2025b)**:

> As a pioneer contribution to the community, we introduce CXR-CounterFact (CCF), a large-scale dataset comprising 320,416 meticulously curated counterfactual reasoning trajectories derived from MIMIC-CXR.

While the details of methodology differ, the overall framing, structure, and phrasing appear heavily relied on the previous work, raising concerns about originality in presentation. Many parts of Abstract, Introduction, and main figures look too similar with a few technical modifications.