Capturing Nuanced Preferences: Preference-Aligned Distillation for Small Language Models
Authors: Yanggan Gu, Junzhuo Li, Sirui Huang, Xin Zou, Zhenghua Li, Xuming Hu
Accepted at: acl25

Capturing Nuanced Preferences: Preference-Aligned Distillation for Small Language Models

Yanggan Gu1,2*  Junzhuo Li1  Sirui Huang1,3  Xin Zou1
Zhenghua Li2†  Xuming Hu1,4‡
1The Hong Kong University of Science and Technology (Guangzhou)
2Soochow University  3University of Technology Sydney
4The Hong Kong University of Science and Technology
yanggangu@outlook.com, zhli13@suda.edu.cn, xuminghu@hkust-gz.edu.cn

Abstract

Aligning small language models (SLMs) with human values typically involves distilling preference knowledge from large language models (LLMs). However, existing distillation methods model preference knowledge in teacher LLMs by comparing pairwise responses, overlooking the extent of difference between responses. This limitation hinders student SLMs from capturing the nuanced preferences for multiple responses. In this paper, we propose a Preference-Aligned Distillation (PAD) framework, which models teacher’s preference knowledge as a probability distribution over all potential preferences, thereby providing more nuanced supervisory signals. Our insight in developing PAD is rooted in the demonstration that language models can serve as reward functions, reflecting their intrinsic preferences. Based on this, PAD comprises three key steps: (1) sampling diverse responses using high-temperature; (2) computing rewards for both teacher and student to construct their intrinsic preference; and (3) training the student’s intrinsic preference distribution to align with the teacher’s. Experiments on four mainstream alignment benchmarks demonstrate that PAD consistently and significantly outperforms existing approaches, achieving over 20% improvement on AlpacaEval 2 and Arena-Hard, indicating superior alignment with human preferences. Notably, on MT-Bench, using the GEMMA model family, the student trained by PAD surpasses its teacher, further validating the effectiveness of our PAD.

1 Introduction

Recently, small language models (SLMs) have demonstrated impressive performance across a variety of tasks (Grattafiori et al., 2024; Riviere et al.,

*This work was completed during the research assistant internship at the Hong Kong University of Science and Technology (Guangzhou).
†Corresponding authors.

![Comparison of the Teacher-as-Annotator methods and our PAD, where “A > B” means the LLM prefers response A over B.](page_1012_370_388_312.png)

Figure 1: Comparison of the Teacher-as-Annotator methods and our PAD, where “A > B” means the LLM prefers response A over B.

2024; Jiang et al., 2023). Compared to large language models (LLMs) such as GPT4 (OpenAI, 2024), SLMs, with their fewer parameters, offer greater efficiency for deployment in diverse applications. However, their smaller parameter number limits their capacity to capture the nuances of human preferences, posing challenges in generating responses that align with human values, such as providing harmless replies to extreme or sensitive questions (Tunstall et al., 2024).

Unlike SLMs, LLMs exhibit superior alignment with human preferences (OpenAI, 2024; Georgiev et al., 2024). Consequently, existing works leverage LLMs as teachers to distill preference knowledge into student SLMs (Bai et al., 2022; Cui et al., 2023; Tunstall et al., 2024; Wang et al., 2024; Yuan et al., 2024). All these works typically encode preference knowledge in teacher LLMs by comparing pairwise responses. For example, Bai et al. (2022) uses teacher-annotated responses to train a reward model, which guides the student via reinforcement learning. Similarly, Tunstall et al. (2024) employs a teacher model for preference annotation but instead applies Direct Preference Optimization (Rafailov et al., 2023) to optimize the student model.
However, the supervision signals provided by these “Teacher-as-Annotator” methods consider only the ordering between responses, disregarding the extent to which one response is preferred over another. As illustrated in Figure 1, in Scenario 1, response A is only slightly better than B by providing more informative details; whereas in Scenario 2, response B contains harmful content (in red), making the difference with A more significant. Nonetheless, both scenarios are uniformly represented as \( A \succ B \). This simplified treatment overlooks the differences between preference pairs, thereby negatively impacting their generations after preference learning (Amini et al., 2024).

To address the limitation, we propose a Preference-Aligned Distillation (PAD) framework, in which preference knowledge is encoded as a probability distribution over all potential preferences, providing subtle supervisory signals from the teacher model. Our insight behind PAD derives from the demonstration that the average log-likelihood of language models can act as reward functions, reflecting their intrinsic preference. Based on this insight, our PAD consists of three steps: 1) Sampling a diverse list of responses from the student model using high temperature; 2) Calculating rewards for each response using both the teacher and student models, and calibrating these rewards using selection probabilities from multiple-choice questions prompting; 3) Enumerating all potential preferences and computing the overall distribution based on the rewards, allowing the student to learn and mimic the teacher’s preference distribution. As illustrated in Figure 1, PAD provides more precise signals, distinguishing the subtle difference in Scenario 1 and significantly differentiating safe and harmful responses in Scenario 2. To enhance PAD’s efficiency, we introduce a Preference Decomposing Strategy, which breaks distillation into multiple rounds to accelerate the training process. Comprehensive experiments across four benchmarks, including Alpaca Eval 2, Arena-Hard, MT-Bench, and GSM8K, with the GEMMA-2 and LLAMA-3 families, demonstrate that PAD consistently outperforms existing approaches, effectively aligning SLMs with human preferences.

Code is available\footnote{https://anonymous.4open.science/r/PAD-E8C6.}, and our main contributions can be summarized as follows:

• We propose a Preference-Aligned Distillation (PAD) framework, which moves beyond pairwise preference by modeling the full preference distribution, enabling the student to capture the teacher’s nuanced preferences.

• We are the first to demonstrate that the average log-likelihood of language models can directly serve as reward functions, capturing the model’s intrinsic preference.

• Experimental results across four benchmarks show that our PAD outperforms existing approaches, suggesting that PAD more precisely captures human preferences.

2 Background

This section reviews two topics: 1) Preference modeling in preference learning theory, and 2) The generation process of language models under the reinforcement learning framework.

Preference Modeling Given a prompt \( x \in \mathcal{X} \), the language model \( \pi \) generates pairs of responses \( (\mathbf{y}_1, \mathbf{y}_2) \sim \pi(\mathbf{y} \mid x) \). A possible preference can be denoted as \( \mathbf{y}_1 \succ \mathbf{y}_2 \mid x \), where \( \mathbf{y}_1 \) and \( \mathbf{y}_2 \) represent the preferred and dispreferred responses. Preferences are assumed to be generated based on a reward model \( r(\mathbf{y} \mid x) \), which assigns a continuous reward \( r \) to each response \( \mathbf{y} \). For simplicity, we omit \( x \) and use \( r(\mathbf{y}) \) to denote \( r(\mathbf{y} \mid x) \).

The pairwise preference probability \( p(\mathbf{y}_1 \succ \mathbf{y}_2 \mid x) \) can be modeled using the Bradley-Terry (BT) framework (Bradley and Terry, 1952) as follows:

\[
p(\mathbf{y}_1 \succ \mathbf{y}_2 \mid x) = \frac{\exp(r(\mathbf{y}_1))}{\exp(r(\mathbf{y}_1)) + \exp(r(\mathbf{y}_2))}.
\]

Now, consider a more generalized scenario with a list of \( n \) responses, denoted as \( Y_n = \{ \mathbf{y}_i \}_{i=1}^n \), and the corresponding list of reward \( R_n = \{ r_i \}_{i=1}^n \). A possible preference ranking \( \tau_n = \mathbf{y}^{(1)} \succ \cdots \succ \mathbf{y}^{(i)} \succ \cdots \succ \mathbf{y}^{(n)} \mid x \), where \( \mathbf{y}^{(i)} \) denotes the response ranked at the \( i \)-th position. Using the Plackett-Luce ranking model (Plackett, 1975; Luce, 2012), the preference probability is defined as:

\[
p(\tau_n) = \prod_{i=1}^n \frac{\exp(r(\mathbf{y}^{(i)}))}{\sum_{j=i}^n \exp(r(\mathbf{y}^{(j)}))}.
\]

Text Generation as a Markov Decision Process (MDP) The text generation process can be modeled as an MDP, which is represented by the triple \( (\mathcal{S}, \mathcal{V}, u)^2 \), where the state space \( \mathcal{S} \) represents all

\footnotetext{2We omit the transition dynamics \( T \) for simplicity. In text generation, these dynamics are deterministic, as each state-action pair uniquely determines the next state.}
possible partially generated sequences, and the action space \( \mathcal{V} \) corresponds to the vocabulary in the language model. At each step \( t \), an action \( y_t \in \mathcal{V} \) (a token) is taken based on the current state \( s \in \mathcal{S} \) (the partially generated sequence) and gains a step (token)-level reward \( u \).

3 Self-Derived Log-Likelihood Rewards

This section introduces how we derive a reward function from language models without any reference model, providing the theoretical foundation for the framework proposed in the next section.

Inverse Reinforcement Learning (IRL) To induce the token-level reward model \( u \), we follow the maximum-entropy IRL framework (Ziebart et al., 2008; Chan and van der Schaar, 2021), where the Q-value function at step \( t \) is defined as:

\[
Q(y_t \mid y_{<t}, x) = \\
u(y_t \mid y_{<t}, x) + \log \sum_{y_{t+1}} \exp[Q(y_{t+1} \mid y_{\leq t}, x)].
\] (3)

Following Hao et al. (2022), we parameterize the Q-function as \( Q(\cdot) = f_\pi(\cdot) \), where \( f_\pi(\cdot) \) represents the output logits of the language model \( \pi \). The reward function \( u \) at each step \( t \) is then defined as

\[
u(y_t \mid y_{<t}, x) = f_\pi(y_t \mid y_{<t}, x) \\
- \log \sum_{y_{t+1} \in \mathcal{V}} \exp[f_\pi(y_{t+1} \mid y_{\leq t}, x)]
\] (4)

We further define \( f_t := f_\pi(y_t \mid y_{<t}, x) \) and \( Z_t := \sum_{y_t \in \mathcal{V}} \exp(f_\pi(y_t \mid y_{\leq t-1}, x)) \) for simplicity, which allows us to write that \( u(y_t \mid y_{<t}, x) = f_t - \log Z_{t+1} \). Please note that at last step, i.e., \( t = |y| \), we have \( \log Z_{|y|+1} = 0 \) according to the definition of the Q-value.

Cumulative Log-Likelihood Reward Given the token-level reward function \( u \), the sequence-level reward is naturally defined by cumulating the token-level rewards:

\[
r(y \mid x) = \sum_{t=1}^{|y|} u(y_t \mid y_{<t}, x) = \sum_{t=1}^{|y|} (f_t - \log Z_{t+1}) \\
= \sum_{t=1}^{|y|} (f_t - \log Z_t) + \log Z_1 - \log Z_{|y|+1} \\
= \sum_{t=1}^{|y|} \log p_\pi(y_t \mid y_{<t}, x) + \log Z_1 \\
= \log p_\pi(y \mid x) + \log Z_1,
\] (5)

where \( p_\pi(y_t \mid y_{<t}, x) \) is the probability of token \( y_t \) given the previous sequences \( (y_{<t}, x) \). Please note that \( \log Z_1 \) does not depend on the particular sequence \( y \).

Normalized Log-Likelihood Reward By combining the Plackett-Luce model in Eq. 2 with the cumulative reward in Eq. 5, the probability for preference \( \tau_n \) is given by:

\[
p(\tau_n) = \prod_{i=1}^n \frac{\exp \left( \log p_\pi(y^{(i)} \mid x) \right)}{\sum_{j=1}^n \exp \left( \log p_\pi(y^{(j)} \mid x) \right)}.
\] (6)

When modeling preferences, the term \( \log Z_1 \) can be eliminated due to the translation invariance property of the softmax function. Therefore, the cumulative reward simplifies to:

\[
r(y \mid x) = \frac{1}{|y|} \log p_\pi(y \mid x).
\] (7)

where \( 1/|y| \) is a length-normalized term to avoid bias towards longer sequences (Meng et al., 2024; Gu et al., 2024).

In other words, the reward of a language model can be formalized as the average log-likelihood, which naturally reflects the inherent preferences of the language model. Specifically, the higher the probability the model assigns to generating a response \( y \), the greater the associated reward.\footnote{Existing works (Rafailov et al., 2023; Gu et al., 2024) also propose constructing reward functions using language models. However, the reward functions we present offer advantages in training efficiency and performance, which are discussed and compared theoretically in Appendix D.}

4 PAD: Preference-Aligned Distillation

This section outlines our PAD, which involves three key training phases (\S 4.1-4.3), followed by the introduction of a preference decomposition strategy to accelerate the training process (\S 4.4).

4.1 Diverse Response Generation

As the first step, taking prompt \( x \) as input, we directly sample \( n \) responses \( Y_n \) from the student model \( \pi^{\text{stu}} \) through repeated sampling. To enhance response diversity, we apply a higher decoding temperature of 0.8. This approach offers two key advantages. First, enabling the generation of higher-quality responses. Existing works have shown that as the number of repeated samples increases, the likelihood of the model generating better answers across various tasks, such as mathematics and coding (Wang et al., 2023; Rozière et al., 2024; Brown
Figure 2: The overall process of the PAD contains three critical steps. The initial step involves sampling diverse responses with high temperature (§4.1). Next, rewards for both models are computed, where the rewards of the teacher would be calibrated(§4.2). Finally, the student is trained to mimic the teacher’s preference distributions.(§4.3)

et al., 2024)). Second, mitigating the exposure bias. Exposure bias arises from the mismatch between training and inference, where the model is trained on ground truth contexts but relies on its own predictions during inference, leading to error accumulation. Following Gu et al. (2024); Agarwal et al. (2024), we train the student model on self-generated responses to reduce this bias.

4.2 Reward Calculation and Calibration

Given a prompt \( x \) and its corresponding list of responses \( Y_n \) from the previous step, we calculate the rewards for both the teacher and student models for each response \( y_i \in Y_n \) using Equation (7). These rewards, denoted as \( r^{\text{tch}}(y_i) \) and \( r^{\text{stu}}(y_i) \), represent the models’ average log-likelihood for each response. However, language models often suffer from miscalibration, where the assigned likelihoods do not accurately reflect the actual quality of the sequences (Zhao et al., 2023). For instance, phrases such as "pros and cons" and "cons and pros" convey the same meaning, but the former may be more frequent in the training data, leading the model to assign it a higher probability. This miscalibration poses a challenge: if the teacher’s reward is miscalibrated, aligning the student model to the teacher may propagate this issue.

To address this, we leverage insights from Ren et al. (2023a) and Ren et al. (2023b), who demonstrate that Multiple-Choice Question (MCQ) selection probabilities better capture response quality than sequence likelihoods. We introduce the MCQ selection probability to calibrate the teacher model’s reward.\footnote{A discussion of other calibration methods is provided in Appendix E.} Specifically, each response \( y_i \in Y_n \) is randomly mapped to a choice within a pre-defined set \( C_n \) (e.g., \( C_3 = \{ 'A', 'B', 'C' \} \)), and we compute the (token-level) probability of selecting each choice:

\[
p_{\text{sel}}(y_i) = p(c_i \mid Y_n, C_n, x),
\]

where \( c_i \) corresponds to the choice associated with response \( y_i \).

We then calibrate the reward for each response by combining the normalized log-likelihood reward with the selection probability:

\[
r^{\text{tch}}(y) = (1 - \alpha)r^{\text{tch}}(y) + \alpha \log p_{\text{sel}}(y),
\]

where the reward calibration ratio \( \alpha \in [0, 1] \) is a hyperparameter that balances the influence of the original reward and the MCQ selection probability.

4.3 Preference Distillation

Based on different ways of modeling teacher preferences, we employ two losses to distillation: the vanilla preference loss \( \mathcal{L}_{\text{VPD}} \), and the probabilistic preference loss \( \mathcal{L}_{\text{PPD}} \).

Vanilla Preference Distillation (VPD) Following Rafailov et al. (2023); Song et al. (2024), the preference is modeled as a unique ranking. Specifically, we obtain ranking \( \tau_n \) of the responses \( Y_n \) by sorting them according to their rewards \( r^{\text{tch}} \). The student model is then trained with negative log-likelihood (NLL) loss to maximize the probability of teacher preference using Eq. 6.

\[
\mathcal{L}_{\text{VPD}} = \sum_{i=1}^{n} \log \frac{\exp \left( \beta r^{\text{stu}}(y^{(i)}) \right)}{\sum_{j=1}^{n} \exp \left( \beta r^{\text{stu}}(y^{(j)}) \right)},
\]

where \( \beta \) is a hyperparameter that controls the scaling of the reward difference.
Probabilistic Preference Distillation (PPD)  Inspired by Cao et al. (2007), we treat the teacher’s rewards as uncertain indicators of preference, which means any preference ranking is assumed to be possible but has a different likelihood.

The preference distribution over all possible rankings for the teacher is expressed as:

\[
\forall \tau_n \in \mathcal{T},\ p_{\pi^{\text{tch}}}(\tau_n) = \prod_{i=1}^{n} \frac{\exp\left(\beta \hat{r}^{\text{tch}}(y^{(i)})\right)}{\sum_{j=i}^{n} \exp\left(\beta \hat{r}^{\text{tch}}(y^{(j)})\right)}.
\]

where \( \mathcal{T} \) denotes the set of all possible rankings. The student’s preference distribution \( p_{\pi^{\text{stu}}}(\tau_n) \) is modeled similarly.

We then employ the Jensen-Shannon divergence (JSD) loss to align the student’s and teacher’s preference distributions:

\[
\mathcal{L}_{\text{PPD}} = \frac{1}{2} \left[ \mathcal{D}_{\text{KL}}(\pi^{\text{tch}}||\pi^{\text{mix}}) + \mathcal{D}_{\text{KL}}(\pi^{\text{stu}}||\pi^{\text{mix}}) \right],
\]

where mixed distribution \( \pi^{\text{mix}} = (\pi^{\text{tch}} + \pi^{\text{stu}})/2 \), and \( \mathcal{D}_{\text{KL}}(\cdot||\cdot) \) is the Kullback-Leibler divergence (KLD). Specifically, the KLD between the teacher’s preference distribution and the mixed distribution is defined as:

\[
\mathcal{D}_{\text{KL}}(\pi^{\text{tch}}||\pi^{\text{mix}}) = \sum_{\tau_n \in \mathcal{T}} p_{\pi^{\text{tch}}}(\tau_n) \log \frac{p_{\pi^{\text{tch}}}(\tau_n)}{p_{\pi^{\text{mix}}}(\tau_n)}.
\]

Similarly, \( \mathcal{D}_{\text{KL}}(\pi^{\text{stu}}||\pi^{\text{mix}}) \) is computed in the same way. By aligning the student’s preference distribution with the teacher’s, the student model not only learns the correct preference ranking but also captures the teacher’s confidence in these rankings.

4.4 Preference Decomposing Strategy

In our PAD, the number of sampled responses, i.e., the sample size \( n \), plays a pivotal role. A larger \( n \) allows for a more macro comparison among responses, reduces the variance introduced by sampling, and increases the likelihood of generating high-quality responses (Brown et al., 2024). However, as \( n \) increases, the computational cost of both sampling and forward propagation also rises. Particularly when modeling preference distributions, the complexity grows factorially, making the computation unfeasible when \( n \) becomes large.

To reduce the computational cost, we propose a preference decomposition strategy. This strategy breaks down the preference of a large batch of responses into the preferences of multiple smaller batches, allowing the training process to be split into several iterative rounds, thereby reducing the overall computational load.

Decomposing Preference Modeling  Given a preference ranking \( \tau_n \), we define a preference decomposition function \( \phi \) to decompose it into \( k \) sub-preferences, such that \( \phi(\tau_n) = \{ \tau_m^{(1)}, \tau_m^{(2)}, \ldots, \tau_m^{(k)} \} \). Assuming that these sub-preferences are independent, we simplify the probability of the complete preference to the probability of the decomposed preferences as follows:

\[
p(\tau_n) \xrightarrow{\text{simplify}} p(\phi(\tau_n)) = \prod_{i=1}^{k} p(\tau_m^{(i)}).
\]

Hence, we use decomposed preferences as the learning objective, for VPD, its NLL loss for decomposed preferences as

\[
\log p(\phi(\tau_n)) = \sum_{i=1}^{k} \log p(\tau_m^{(i)}).
\]

This shows that the distillation loss for a large batch of responses is equivalent to the sum of losses over multiple smaller batches.\footnote{A similar decomposition can be applied to Preference Propagation Decoding (PPD), with the detailed proof provided in Appendix A.} Based on this insight, we adopt the *Iterative Distillation Process*, as depicted in Figure 3. In this approach, the distillation process is divided into \( k \) iterations, each applied to smaller batches of size \( m \). This reduces the complexity of modeling the preference distribution from \( O(n!) \) to \( O(k \cdot m!) \), thereby significantly lowering the computational cost of training.

![Diagram showing the Iterative Distillation Process, with steps for sampling, reward calculation, training, and next iteration.](page_1012_186_393_312.png)

Figure 3: Iterative Distillation Process.

5 Experiment

5.1 Setup

Models  We evaluate two model families in our main experiments: 1) GEMMA-2 Models\footnote{https://ai.google.dev/gemma} (Riviere et al., 2024) include GEMMA-2-9B-IT as
<table>
  <tr>
    <th rowspan="2">Model Families</th>
    <th rowspan="2">Method</th>
    <th colspan="2">Alpaca-Eval 2.0</th>
    <th colspan="2">Arena-Hard</th>
    <th colspan="2">MT-Bench</th>
    <th rowspan="2">GSM8K</th>
  </tr>
  <tr>
    <th>LC (%)</th>
    <th>WR (%)</th>
    <th>WR (%)</th>
    <th>Score (1~10)</th>
    <th>Acc. (%)</th>
  </tr>
  <tr>
    <td rowspan="6">GEMMA-2</td>
    <td>Teacher (9B)</td>
    <td>55.27</td>
    <td>42.50</td>
    <td>61.16</td>
    <td>6.99</td>
    <td>87.41</td>
    <td></td>
  </tr>
  <tr>
    <td>Student (2B)</td>
    <td>39.51</td>
    <td>41.99</td>
    <td>37.55</td>
    <td>6.70</td>
    <td>51.63</td>
    <td></td>
  </tr>
  <tr>
    <td>Standard KD</td>
    <td>41.67 (±2.2)</td>
    <td>45.24 (±3.3)</td>
    <td>52.36 (±14.8)</td>
    <td>6.78 (±0.1)</td>
    <td>54.37 (±2.7)</td>
    <td></td>
  </tr>
  <tr>
    <td>SeqKD</td>
    <td>42.91 (±3.4)</td>
    <td>46.44 (±4.4)</td>
    <td>54.87 (±17.3)</td>
    <td>6.88 (±0.2)</td>
    <td>55.72 (±4.1)</td>
    <td></td>
  </tr>
  <tr>
    <td>MiniLLM</td>
    <td>42.97 (±3.5)</td>
    <td>48.32 (±6.3)</td>
    <td>55.75 (±18.2)</td>
    <td>6.88 (±0.2)</td>
    <td>55.26 (±3.6)</td>
    <td></td>
  </tr>
  <tr>
    <td>DPO</td>
    <td>43.77 (±4.3)</td>
    <td>54.02 (±12.0)</td>
    <td>57.43 (±19.9)</td>
    <td>6.87 (±0.2)</td>
    <td>57.07 (±5.4)</td>
    <td></td>
  </tr>
  <tr>
    <td>SimPO</td>
    <td>44.94 (±5.4)</td>
    <td>54.16 (±12.2)</td>
    <td>58.64 (±21.1)</td>
    <td>6.91 (±0.2)</td>
    <td>57.24 (±5.6)</td>
    <td></td>
  </tr>
  <tr>
    <td>PRO</td>
    <td>45.87 (±6.4)</td>
    <td>56.48 (±14.5)</td>
    <td>58.95 (±21.4)</td>
    <td>6.96 (±0.3)</td>
    <td>58.83 (±7.2)</td>
    <td></td>
  </tr>
  <tr>
    <td>PAD w/ \( \mathcal{L}_{VPD} \)</td>
    <td><b>46.13</b> (±6.6)</td>
    <td><b>57.94</b> (±16.0)</td>
    <td><b>59.07</b> (±21.5)</td>
    <td><b>6.93</b> (±0.2)</td>
    <td><b>59.06</b> (±7.4)</td>
    <td></td>
  </tr>
  <tr>
    <td>PAD w/ \( \mathcal{L}_{PPD} \)</td>
    <td><b>49.62</b> (±10.1)</td>
    <td><b>59.50</b> (±17.5)</td>
    <td><b>60.00</b> (±22.4)</td>
    <td><b>7.02</b> (±0.3)</td>
    <td><b>59.29</b> (±7.7)</td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="6">LLaMA-3</td>
    <td>Teacher (8B)</td>
    <td>37.01</td>
    <td>38.93</td>
    <td>52.66</td>
    <td>7.00</td>
    <td>84.00</td>
    <td></td>
  </tr>
  <tr>
    <td>Student (3B)</td>
    <td>27.82</td>
    <td>29.02</td>
    <td>31.70</td>
    <td>6.42</td>
    <td>57.09</td>
    <td></td>
  </tr>
  <tr>
    <td>Standard KD</td>
    <td>29.11 (±1.3)</td>
    <td>29.60 (±0.6)</td>
    <td>41.68 (±10.0)</td>
    <td>6.49 (±0.1)</td>
    <td>59.15 (±2.1)</td>
    <td></td>
  </tr>
  <tr>
    <td>SeqKD</td>
    <td>29.48 (±1.7)</td>
    <td>30.04 (±1.0)</td>
    <td>42.52 (±10.8)</td>
    <td>6.53 (±0.1)</td>
    <td>60.94 (±3.8)</td>
    <td></td>
  </tr>
  <tr>
    <td>MiniLLM</td>
    <td>30.05 (±2.2)</td>
    <td>30.38 (±1.4)</td>
    <td>42.21 (±10.5)</td>
    <td>6.67 (±0.3)</td>
    <td>60.35 (±3.3)</td>
    <td></td>
  </tr>
  <tr>
    <td>DPO</td>
    <td>31.42 (±2.6)</td>
    <td>32.01 (±3.0)</td>
    <td>44.71 (±13.0)</td>
    <td>6.62 (±0.2)</td>
    <td>61.63 (±4.5)</td>
    <td></td>
  </tr>
  <tr>
    <td>SimPO</td>
    <td>32.74 (±4.9)</td>
    <td>32.46 (±3.4)</td>
    <td>44.85 (±13.2)</td>
    <td>6.73 (±0.3)</td>
    <td>61.22 (±4.1)</td>
    <td></td>
  </tr>
  <tr>
    <td>PRO</td>
    <td>32.11 (±4.3)</td>
    <td>32.23 (±3.2)</td>
    <td>45.09 (±13.4)</td>
    <td>6.71 (±0.3)</td>
    <td>61.47 (±4.4)</td>
    <td></td>
  </tr>
  <tr>
    <td>PAD w/ \( \mathcal{L}_{VPD} \)</td>
    <td>32.71 (±4.9)</td>
    <td>32.34 (±3.3)</td>
    <td>45.23 (±13.5)</td>
    <td>6.77 (±0.3)</td>
    <td>61.35 (±4.3)</td>
    <td></td>
  </tr>
  <tr>
    <td>PAD w/ \( \mathcal{L}_{PPD} \)</td>
    <td><b>33.61</b> (±5.8)</td>
    <td><b>32.55</b> (±3.5)</td>
    <td><b>46.73</b> (±15.0)</td>
    <td><b>6.84</b> (±0.4)</td>
    <td><b>62.24</b> (±5.1)</td>
    <td></td>
  </tr>
</table>

Table 1: Main results with the Gemma-2 and LLaMA-3 Models.

teacher and GEMMA-2-2B-IT as the student, and 2) LLaMA-3 Models7 (Grattafiori et al., 2024) includes LLaMA-3.1-8B-INSTRUCT as teacher and LLaMA-3.2-3B-INSTRUCT as student.

Training Training data is sourced from ULTRA-FEEDBACK8 (Cui et al., 2023), comprising around 60k preference samples across a diverse range of tasks, including mathematical reasoning and open-ended writing. We filter out samples that exceed the models’ context length, set the number of sampled responses \( n = 4 \), and apply a reward calibration ratio \( \alpha = 0.8 \) to mitigate teacher model bias. Training is conducted for one epoch by default, with further details provided in Appendix B.1.

Evaluation We assess our models on four benchmarks: AlpacaEval 2.0 (Li et al., 2023), MT-Bench (Zheng et al., 2023), Arena-Hard (Li et al., 2024), and GSM8K (Cobbe et al., 2021), which evaluate a model’s conversational versatility across a range of tasks. For AlpacaEval, we report both the raw win rate (WR) and the length-controlled win rate (LC), the latter being robust to verbosity. For Arena-Hard, we report the win rate (WR)9. For MT-Bench, we present the average MT-Bench score, evaluated by GPT-4 Turbo. Detailed evaluation settings can be found in Appendix B.2.

Baselines We compare PAD with two types of baselines: 1) Traditional Knowledge Distillation, which aims to learn the teacher’s distribution at the logits level, including Standard KD (Hinton et al., 2015), SeqKD (Kim and Rush, 2016), and MiniLLM (Gu et al., 2024); 2) Preference Knowledge Distillation, which aims to transfer the teacher’s preference knowledge to the student model. Under the “Teacher-as-annotator” paradigm, we choose DPO (Tunstall et al., 2024), SimPO (Meng et al., 2024), and PRO (Song et al., 2024) as baselines. A detailed description of these baselines can be found in Appendix B.3.

5.2 Main Result

Table 1 summarizes the experimental results across several benchmarks. The primary finding is that the student models trained with PAD consistently outperform both their initial counterparts and existing approaches. Specifically, PAD achieves over a 20% improvement on Alpaca-Eval 2.0 and Arena-Hard, highlighting its ability to more effectively capture the teacher’s preferences, thereby aligning more closely with human values.

Preference distillation methods offer significant advantages over traditional KD methods. Traditional KD approaches, such as Standard KD and
<table>
  <tr>
    <th rowspan="2">Strategy</th>
    <th colspan="2">Alpaca-Eval 2</th>
    <th rowspan="2">GPU Hours</th>
  </tr>
  <tr>
    <th>LC (%)</th>
    <th>A800</th>
  </tr>
  <tr>
    <td colspan="4"><i>Total Sample Size: 4</i></td>
  </tr>
  <tr>
    <td>1 × 4</td>
    <td>49.42</td>
    <td></td>
    <td>12.32</td>
  </tr>
  <tr>
    <td>2 × 2</td>
    <td>48.94 (±0.5)</td>
    <td></td>
    <td>12.35 (±0.0%)</td>
  </tr>
  <tr>
    <td colspan="4"><i>Total Sample Size: 8</i></td>
  </tr>
  <tr>
    <td>1 × 8</td>
    <td>50.57</td>
    <td></td>
    <td>31.51</td>
  </tr>
  <tr>
    <td>2 × 4</td>
    <td>51.42 (±0.8)</td>
    <td></td>
    <td>27.98 (±12%)</td>
  </tr>
  <tr>
    <td colspan="4"><i>Total Sample Size: 12</i></td>
  </tr>
  <tr>
    <td>1 × 12</td>
    <td>51.55</td>
    <td></td>
    <td>52.74</td>
  </tr>
  <tr>
    <td>3 × 4</td>
    <td>52.67 (±1.1)</td>
    <td></td>
    <td>42.07 (±20%)</td>
  </tr>
</table>

Table 2: Decomposition Strategy Improves Performance While Reducing Cost. “Strategy” such as “1 × 4” indicates that the number of training iterations is 1, and the sample size per iteration is 4.

SeqKD, yield modest improvements (1-3%) in Alpaca-Eval 2.0 LC scores. In contrast, preference distillation methods like DPO and SimPO show more substantial gains, particularly in aligning with human preferences, as evidenced in Alpaca-Eval 2.0 and Arena-Hard benchmarks. These findings align with the work of Tunstall et al. (2024).

Notably, PAD with \( \mathcal{L}_{\text{PPD}} \) outperforms PAD with \( \mathcal{L}_{\text{VPD}} \) across multiple benchmarks, including Alpaca-Eval 2.0 LC (49.62 vs. 46.13) and MT-Bench (7.02 vs. 6.93). For the GEMMA-2 model family, the student trained with \( \mathcal{L}_{\text{PPD}} \) even surpasses its teacher in MT-Bench, scoring 7.02 compared to the teacher’s 6.99. The key advantage of PPD over VPD lies in its preference modeling approach: by capturing the full preference distribution rather than just a simple ranking, PPD provides more nuanced supervisory signals, thereby better reflecting subtle human preferences—an aspect often overlooked by existing distillation methods.

5.3 Analysis

We analyze the impact of our proposed Preference Decomposing Strategy and Reward Calibration. To assess the generalization capability of PAD, we further investigate its performance when the teacher and student models belong to different families. A more detailed analysis is provided in Appendix C.

Effect of Preference Decomposing Strategy We investigated the impact of the iterative distillation process on performance and training time using the preference decomposing strategy. Table 2 presents the effects of varying iteration counts and sample sizes. For a sample size of 4, decomposing the sampling process into two iterative steps does not reduce training time, as the low complexity of modeling the distribution with fewer samples renders the effect negligible. However, when the sample size increases to 12, adopting a three-iteration decomposition reduces training time by 20%, demonstrating that the preference decomposing strategy becomes more advantageous as the sample size grows, thereby accelerating training. Notably, decomposing the sampling into multiple iterations does not result in a significant performance drop, indicating that the strategy effectively balances efficiency with stable performance.

Additionally, we examined the iterative distillation process as a continuous learning method. As shown in Figure 4, we compared three high-performing baseline methods—DPO, SimPO, and PRO. The results indicate that the iterative distillation process consistently improves performance across all methods, with PAD achieving the best results, highlighting its superior effectiveness.

![Line plot showing LC (%) vs. Iteration for different methods](page_1012_186_420_312.png)

Figure 4: Alpaca-Eval LC with different iterations.

<table>
  <tr>
    <th>Method</th>
    <th>Calibration</th>
    <th>Alpaca-Eval LC (%)</th>
  </tr>
  <tr>
    <td>Student</td>
    <td>-</td>
    <td>39.51</td>
  </tr>
  <tr>
    <td>PAD w/ \( \mathcal{L}_{\text{VPD}} \)</td>
    <td>No</td>
    <td>40.36 (±0.8)</td>
  </tr>
  <tr>
    <td>PAD w/ \( \mathcal{L}_{\text{VPD}} \)</td>
    <td>Yes</td>
    <td>46.13 (±6.6)</td>
  </tr>
  <tr>
    <td>PAD w/ \( \mathcal{L}_{\text{PPD}} \)</td>
    <td>No</td>
    <td>41.59 (±2.0)</td>
  </tr>
  <tr>
    <td>PAD w/ \( \mathcal{L}_{\text{PPD}} \)</td>
    <td>Yes</td>
    <td>49.62 (±10.1)</td>
  </tr>
</table>

Table 3: Ablation study on the Reward Calibration

Influence of Reward Calibration We conduct ablation experiments on GEMMA-2-2B-IT with Reward Calibration, as summarized in Table 3. The results show a significant performance improvement, with an 8.03 LC gain on PAD with \( \mathcal{L}_{\text{PPD}} \). To further assess the impact of reward calibration, we vary the calibration ratio \( \alpha \) and examine its effect on performance, as depicted in Figure 5. Increasing \( \alpha \) leads to consistent performance gains, peaking at \( \alpha = 0.8 \). Beyond this point, further increases in \( \alpha \) result in diminishing returns. For applications with lower performance demands, setting \( \alpha = 1 \) offers a practical trade-off by avoiding the computational cost of calculating the teacher’s log-likelihood.
Figure 5: Alpaca-Eval LC Win Rate with different \( \alpha \)

<table>
  <tr>
    <th>Method</th>
    <th colspan="2">Alpaca-Eval LC (%)</th>
  </tr>
  <tr>
    <th></th>
    <th>LLaMA</th>
    <th>Qwen</th>
  </tr>
  <tr>
    <td>Student</td>
    <td>27.82</td>
    <td>9.37</td>
  </tr>
  <tr>
    <td>DPO</td>
    <td>49.42 (\( \uparrow 21.6 \))</td>
    <td>16.66 (\( \uparrow 7.3 \))</td>
  </tr>
  <tr>
    <td>SimPO</td>
    <td>49.78 (\( \uparrow 22.0 \))</td>
    <td>18.08 (\( \uparrow 8.7 \))</td>
  </tr>
  <tr>
    <td>PRO</td>
    <td>50.18 (\( \uparrow 22.4 \))</td>
    <td>18.57 (\( \uparrow 9.2 \))</td>
  </tr>
  <tr>
    <td>PAD w/ \( \mathcal{L}_{VPD} \)</td>
    <td>50.45 (\( \uparrow 22.6 \))</td>
    <td>18.83 (\( \uparrow 9.4 \))</td>
  </tr>
  <tr>
    <td>PAD w/ \( \mathcal{L}_{PPD} \)</td>
    <td><b>51.96</b> (\( \uparrow 24.1 \))</td>
    <td><b>20.57</b> (\( \uparrow 11.2 \))</td>
  </tr>
</table>

Table 4: Heterogeneous Distillation Study. We use GEMMA-2-9B-1T as the teacher and LLaMA-3.2-3B-INSTRUCT/Qwen-2.5-1.5B-INSTRUCT as the student.

Heterogeneous Study Previous experiments typically involve teacher and student models from the same family, sharing a common vocabulary and similar architectures. To evaluate the generalization ability of our PAD method, we extend these experiments by using teacher and student models from different families. As shown in Table 4, our approach consistently outperforms existing methods, demonstrating robust generalization. Furthermore, a comparison with results in Table 1 reveals that using a stronger teacher model, GEMMA-9B, significantly improves the performance of the student model (LLaMA-3B). Specifically, with GEMMA-9B as the teacher, the student achieves an LC of 51.96%, compared to only 33.61% with LLaMA-8B. This suggests a positive correlation between the teacher’s performance and the student’s post-distillation performance, aligning with findings from Gu et al. (2024); Lee et al. (2024).

6 Related Work

Traditional Knowledge Distillation Knowledge distillation (KD), introduced by Hinton et al. (2015), primarily aims at model compression by training a smaller student model to mimic the output behavior of a larger teacher model (Kim and Rush, 2016; Liang et al., 2021; Zhang et al., 2023; Gu et al., 2024; Agarwal et al., 2024). Kim and Rush (2016) extended KD to machine translation by training students on sequences generated by teachers in order to imitate teacher behavior.

More recently, Gu et al. (2024) advanced KD using reverse KL divergence on student -generated sequence to mitigate exposure bias, improving student model performance. A key feature of these methods is that distillation is performed over the shared vocabulary of both teacher and student models. Our PAD eliminates this limitation, enabling effective distillation with different vocabularies.

Preference Knowledge Distillation Motivated by the observation that large models have achieved a high degree of alignment with human values and preferences, many efforts focus on distilling preference knowledge from large models to smaller ones (Bai et al., 2022; Cui et al., 2023; Lee et al., 2024; Yuan et al., 2024; Tunstall et al., 2024; Yang et al., 2024). Bai et al. (2022) first introduced this concept, also known as Reinforcement Learning from AI Feedback (RLAIF), where teacher models annotate response pairs from the student to create a preference dataset for training a reward model. Tunstall et al. (2024) further utilized teacher-annotated preferences with Direct Preference Optimization (DPO) (Rafailov et al., 2023), streamlining the training of student models. These approaches follow the "Teacher-as-Annnotator" paradigm. The annotated preference datasets generated through this paradigm can be directly employed with methods such as DPO, SimPO (Meng et al., 2024), and PRO (Song et al., 2024), enabling preference optimization of student models. However, a significant limitation of these methods lies in their reliance on unique ranking, which constrains their ability to model nuanced preferences. In contrast, our PAD treats modeling preference knowledge as a distribution over all possible preferences, enabling nuanced alignment for the student and teacher models.

7 Conclusion

In this paper, we introduced the Preference-Aligned Distillation (PAD) framework, which models the teacher’s preference knowledge as a probability distribution over all potential preferences. This supervisory signal enables the student model to capture subtle distinctions between responses. Experimental results on the GEMMA-2 and LLaMA-3 model families show that PAD outperforms both traditional knowledge distillation and existing preference distillation methods across four benchmark tasks, highlighting its capacity for learning in-depth human preferences.
Limitations

Our research has several limitations. Firstly, the generalization capability is insufficient as we have not conducted experiments on larger-scale teacher and student models, primarily due to limited computational resources. Secondly, sampling multiple responses consumes more computational overhead. However, because SLMs have relatively smaller parameter sizes, this overhead remains comparatively modest. Thirdly, our method requires token-level probabilities, which are unavailable in some black-box models.

Acknowledgements

This work was supported by the Guangdong Provincial Department of Education Project (Grant No.2024KQNCX028); Scientific Research Projects for the Higher-educational Institutions (Grant No.2024312096), Education Bureau of Guangzhou Municipality; Guangzhou-HKUST(GZ) Joint Funding Program (Grant No.2025A03J3957), Education Bureau of Guangzhou Municipality.

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<table>
  <tr>
    <th>hyperparameter</th>
    <th>Value</th>
    <th>Searching Space</th>
  </tr>
  <tr>
    <td>\( \beta \)</td>
    <td>10</td>
    <td>[1, 2, 5, 8, 10]</td>
  </tr>
  <tr>
    <td>batch size</td>
    <td>128</td>
    <td>[32, 64, 128]</td>
  </tr>
  <tr>
    <td>warmup ratio</td>
    <td>0.1</td>
    <td>[0.05, 0.1]</td>
  </tr>
</table>

Table 5: The hyperparameter values in PAD training.

A Decomposing Probabilistic Preference Distillation

Substituting Eq. 13 into the KLD:

\[
\sum_{\tau_n} \left( \prod_{i=1}^k p_{\pi^{\text{ch}}}(\tau_m^{(i)}) \right) \log \left( \frac{\prod_{i=1}^k p_{\pi^{\text{ch}}}(\tau_m^{(i)})}{\prod_{i=1}^k p_{\pi^{\text{mix}}}(\tau_m^{(i)})} \right)
\]

\[
= \sum_{\tau_n} \left( \prod_{i=1}^k p_{\pi^{\text{ch}}}(\tau_m^{(i)}) \right) \left( \sum_{i=1}^k \log \frac{p_{\pi^{\text{ch}}}(\tau_m^{(i)})}{p_{\pi^{\text{mix}}}(\tau_m^{(i)})} \right)
\]

interchange summations,

\[
= \sum_{i=1}^k \sum_{\tau_n} \left( \prod_{j=1}^k p_{\pi^{\text{ch}}}(\tau_m^{(j)}) \right) \log \frac{p_{\pi^{\text{ch}}}(\tau_m^{(i)})}{p_{\pi^{\text{mix}}}(\tau_m^{(i)})}
\]

notice that for a fixed \( i \), the logarithm term only depends on \( \tau_m^{(i)} \), and the product can be separated,

\[
= \sum_{i=1}^k \left( \sum_{\tau_m^{(i)}} p_{\pi^{\text{ch}}}(\tau_m^{(i)}) \log \frac{p_{\pi^{\text{ch}}}(\tau_m^{(i)})}{p_{\pi^{\text{mix}}}(\tau_m^{(i)})} \right) \prod_{j \neq i} \left( \sum_{\tau_m^{(j)}} p_{\pi^{\text{ch}}}(\tau_m^{(j)}) \right)
\]

based on the independence assumption of sub-preferences, \( \sum_{\tau_m^{(j)}} p_{\pi^{\text{ch}}}(\tau_m^{(j)}) = 1 \) for each \( j \),

\[
= \sum_{i=1}^k \sum_{\tau_m^{(i)}} p_{\pi^{\text{ch}}}(\tau_m^{(i)}) \log \frac{p_{\pi^{\text{ch}}}(\tau_m^{(i)})}{p_{\pi^{\text{mix}}}(\tau_m^{(i)})}
\]

Therefore, the KLD can be decomposed as:

\[
\mathcal{D}_{\text{KL}}(p_{\pi^{\text{ch}}}(\tau_n) \| p_{\pi^{\text{mix}}}(\tau_n)) =
\sum_{i=1}^k \mathcal{D}_{\text{KL}}(p_{\pi^{\text{ch}}}(\tau_m^{(i)}) \| p_{\pi^{\text{mix}}}(\tau_m^{(i)}))
\]

The JSD Loss (Eq. 12) used in PPD is the average of two KLDs in different directions, making JSD also decomposable.

B Implementation Details

B.1 Training

We individually search the learning rates for different model families in the range of [3e - 7, 5e - 7, 8e - 7, 1e - 6, 1e - 5]. As a result, the learning rate for GEMMA-2 Models is 8e - 7 and for LLAMA-3 Models is 1e - 6. Table 5 shows other hyperparameters for training. All the training experiments in this paper were conducted on 2×A800 GPUs based on the TRL repo10.

<table>
  <tr>
    <th>Model</th>
    <th>Human Agreement</th>
  </tr>
  <tr>
    <td>GPT4</td>
    <td>69.17</td>
  </tr>
  <tr>
    <td>LLAMA-3.1-70B-INSTRUCT</td>
    <td>69.10</td>
  </tr>
  <tr>
    <td>GPT4-Turbo</td>
    <td>68.09</td>
  </tr>
  <tr>
    <td>LLAMA-3-70B-INSTRUCT</td>
    <td>67.53</td>
  </tr>
  <tr>
    <td>Qwen2.5-72B-INSTRUCT</td>
    <td>67.51</td>
  </tr>
  <tr>
    <td>Humans</td>
    <td>65.66</td>
  </tr>
</table>

Table 6: Leaderboard of judge models in AlpacaEval.

B.2 Evaluation

Data Statistics AlpacaEval 2 consists of 805 questions from five datasets, MT-Bench includes 80 questions across eight categories, and the recently released Arena-Hard is an enhanced version of MT-Bench, comprising 500 challenging questions. Since the training data, ultrafeedback, includes some mathematical reasoning problems, we additionally incorporate the GSM8K test set, which contains approximately 1,300 questions, to evaluate the model’s mathematical abilities.

Judge Models For AlpacaEval 2.0 and Arena-Hard, we employ LLAMA-3.1-70B-INSTRUCT as the judge model. For MT-Bench, we employ GPT-4 Turbo as the judge model. For GSM8K, we report accuracy on the test set. Table 6 presents the evaluation capability test11 of these judge models on AlpacaEval. We can see that LLAMA-3.1-70B-INSTRUCT has evaluation capabilities comparable to GPT4-Turbo.

B.3 Baselines

For traditional knowledge distillation, we consider three baselines: 1) Standard KD (Hinton et al., 2015): Fine-tunes the student model using the teacher model’s logits distribution as a supervision signal, applied to golden responses. 2) SeqKD (Kim and Rush, 2016): Directly fine-tunes the student model with cross-entropy loss using responses generated by the teacher model. 3) MiniLLM (Gu et al., 2024): Employs the teacher model’s logits

10https://github.com/huggingface/trl/tree/main
11https://github.com/tatsu-lab/alpaca_eval/tree/main/src/alpaca_eval/evaluators_configs
distribution as supervision signal while fine-tuning the student model on its own generated responses.

For preference knowledge distillation, under the "Teacher-as-Annotator" paradigm, we employ three offline preference optimization methods as baselines: 1) DPO (Rafailov et al., 2023; Tunstall et al., 2024): Treats the student model as a reward model, fine-tuning it based on a reward function derived from a reference model. 2) SimPO (Meng et al., 2024): Operates similarly to DPO but uses average log-likelihood as the optimization objective. 3) RPO (Song et al., 2024): Extends the above approaches by optimizing with listwise preference information. For a fair comparison, we also use responses sampled from the student model for these baselines. We use the MCQ selection probability introduced in Section 4.2 as the score to rank the responses. For the pairwise preference optimization methods DPO and SimPO, we select the responses with the maximum and minimum rewards to form preference pairs. For the listwise preference optimization method PRO, we directly sort the scores to form the preference ranking. Please kindly note that constructing preference pairs using the maximum and minimum scores of responses is a common practice (Cui et al., 2023; Meng et al., 2024). Moreover, our preliminary experiments indicate that splitting the entire listwise response data into multiple pairwise data and training with DPO/SimPO does not yield significant performance improvements.

C Additional Experiments and Analyses

<table>
  <tr>
    <th>Method</th>
    <th>Alpaca-Eval<br>LC (%)</th>
    <th>Arena-Hard<br>WR (%)</th>
  </tr>
  <tr>
    <td>Teacher</td>
    <td>56.89</td>
    <td>76.09</td>
  </tr>
  <tr>
    <td>Student</td>
    <td>39.51</td>
    <td>37.55</td>
  </tr>
  <tr>
    <td>DPO</td>
    <td>51.93</td>
    <td>65.42</td>
  </tr>
  <tr>
    <td>SimPO</td>
    <td>52.36</td>
    <td>66.36</td>
  </tr>
  <tr>
    <td>PRO</td>
    <td>52.45</td>
    <td>68.01</td>
  </tr>
  <tr>
    <td>PAD w/ \( \mathcal{L}_{VPD} \)</td>
    <td>53.32</td>
    <td>67.38</td>
  </tr>
  <tr>
    <td>PAD w/ \( \mathcal{L}_{PPD} \)</td>
    <td>55.96</td>
    <td>69.90</td>
  </tr>
</table>

Table 7: Scaling-up Study.

Scaling Up We use GEMMA-2-27B-It as the teacher and GEMMA-2-2B-It as the student. The overall performance is shown in Table 7. When employing larger-scale teacher models, our PAD consistently and significantly enhances the ability of small models to align with human preferences. Compared to the main result (§5.2), we observe that when using a more capable teacher, the student model achieves greater performance improvements, indicating that the performance gap between the teacher and student is a key factor in determining the extent of the student’s enhancement.

Effect of Sample Size We investigate the impact of the number of sampled responses on PPD, and the results can be seen in Figure 6. We observe that as the number of sample size \( n \) increases, the performance of the student model improves accordingly. This indicates that obtaining more feedback knowledge through extensive sampling from the text generation space facilitates better alignment of the student model with the teacher’s preferences.

Figure 6: Win Rate with different sample size \( n \).

D Comparative Analysis of Reward Functions

For the preference distillation, existing works DPO(Rafailov et al., 2023) and MiniLLM(Gu et al., 2024) both propose using language models to construct reward functions. However, there are significant differences between these methods and our PAD, particularly in the formulation of the reward function, the theoretical foundation, and how the reward is used during training. Below, we elaborate on three key distinctions.

D.1 Formulation of Reward Functions

DPO (Direct Preference Optimization) constructs the reward function using the log ratio of the likelihoods between the "current model" and the "reference model" for the same response:

\[
r(y) = \log \frac{p_{\text{current}}(y)}{p_{\text{reference}}(y)},
\]

where \( p_{\text{current}} \) represents the likelihood of response \( y \) under the model aligned with human preferences, and \( p_{\text{reference}} \) typically corresponds to the likelihood from a supervised fine-tuning (SFT) model. A higher reward indicates that the current model is more likely to generate \( y \) than the reference model.
<table>
  <tr>
    <th>Formulation</th>
    <th>Preference Expression</th>
    <th>Reference-free</th>
    <th>Aligned with Generation</th>
  </tr>
  <tr>
    <td>DPO</td>
    <td>\( r(y) = \log \frac{p_{\text{current}}(y)}{p_{\text{reference}}(y)} \)</td>
    <td>✓</td>
    <td>×</td>
    <td>×</td>
  </tr>
  <tr>
    <td>MiniLLM</td>
    <td>\( r(y) = \log \frac{p_{\text{teacher}}(y)}{p_{\text{student}}(y)} \)</td>
    <td>×</td>
    <td>×</td>
    <td>×</td>
  </tr>
  <tr>
    <td>PAD (Ours)</td>
    <td>\( r(y) = \frac{1}{|y|} \log p_{\text{current}}(y) \)</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
  </tr>
</table>

Table 8: Theoretical Comparison of Reward Functions

MiniLLM is similar in spirit to DPO but uses a “teacher model” and “student model” in a distillation framework. Its reward function is based on the likelihood ratio:

\[
r(y) = \log \frac{p_{\text{teacher}}(y)}{p_{\text{student}}(y)}.
\]

Here, a larger reward implies that the teacher model is more inclined to generate the sequence than the student model.

PAD (Ours) defines the reward function as the model’s log-likelihood of the response, averaged by the sequence length for consistency across varying lengths:

\[
r(y) = \frac{1}{|y|} \log p_{\text{current}}(y).
\]

When the model assigns a higher probability to a response, that response receives a higher reward.

D.2 Theoretical Foundations and Training Processes

Different Theoretical Foundations. DPO is grounded in the Reinforcement Learning from Human Feedback (RLHF) framework, while MiniLLM derives its reward function from the reverse KL-divergence of the policy gradient. In contrast, PAD is based on Inverse Reinforcement Learning (IRL), where we demonstrate that the average log-likelihood can function as a reward, effectively capturing the model’s intrinsic preferences.

Roles in the Loss Function. DPO aims to directly maximize the margin between "good" and "bad" responses within its loss function. MiniLLM, on the other hand, treats the reward function as a scaling factor for the SFT loss, where responses with higher rewards experience a more significant increase in likelihood. PAD utilizes the reward function to derive a preference distribution over responses, enabling a student model to emulate the teacher model’s preferences. Notably, unlike DPO and PAD, MiniLLM’s reward function does not explicitly encode preference.

Reference Model Requirement. Both DPO and MiniLLM necessitate the simultaneous use of two models (current/reference or teacher/student) for reward computation. In contrast, PAD requires only the log-likelihood of the current model, eliminating the need for a reference model. This simplification not only streamlines the theoretical framework but also reduces computational cost in practice.

Alignment with the Generation Stage. The reward functions in DPO and MiniLLM generally do not align with the probability distribution used during the final text-generation phase. In contrast, PAD’s reward function is directly aligned with the model’s log-likelihood, ensuring consistency between the training and inference.

D.3 Empirical Evaluation

Beyond the theoretical distinctions outlined in Table 8, the choice of reward significantly influences performance across various benchmarks. As illustrated in the main results of Table 1, PAD, which employs average log-likelihood as its reward function, demonstrates superior performance gains.

E Discussion of Reward Calibration

E.1 Existing Methods

Recent studies have shown that large language models (LLMs) are well-calibrated for multiple-choice question-answering and true/false evaluation tasks (Kadavath et al., 2022; OpenAI, 2024; Robinson and Wingate, 2023), suggesting that these models exhibit better calibration on token-level scores. In addition to the MCQ-based calibration method, we also compare it with the \( P(\text{True}) \) method proposed by Kadavath et al. (2022).

\( P(\text{True}) \) involves asking the model whether a candidate answer is correct. If the answer is correct, the model outputs True, otherwise False, with the probability of True representing the likelihood of
the response being correct. Formally, for an input \( x \) and a response \( y \), this probability is given by:

\[
p_{true}(x, y) = p(\text{Yes} \mid x, y).
\]

A variant, **P(True) with reference**, incorporates all candidate responses:

\[
p_{true}(x, y, Y) = p(\text{Yes} \mid x, y, Y).
\]

Notably, the P(True) method is more computationally intensive than the MCQ-based method, as it requires multiple queries to determine the probability values for different responses, whereas MCQ-based calibration can map responses to options and obtain probabilities in a single query.

**E.2 Experimental Comparison**

We conducted experiments on the **Alpaca-eval Evaluator** test set (Li et al., 2023) to assess the alignment of various calibrated reward functions with human preferences. This test set, consisting of approximately 2.5k data points, is commonly used to evaluate LLM performance as judges, where each data point includes a pair of responses ranked by human preferences. We applied different reward calibration methods to rank these preference pairs and evaluated their alignment with human preferences.

The evaluation metrics include *Human Agreement* and *Prob. prefer longer*.

**Human Agreement:** This metric measures the alignment between human and model rankings of response pairs. Given \( n \) response pairs (\( y_g \) for the good response and \( y_b \) for the bad response), the score is calculated as:

\[
\text{score} = 1 - \frac{1}{n} \sum_{i=1}^{n} \left[ \left| \text{rank}_i^{\text{human}}(y_g) - \text{rank}_i^{\text{pred}}(y_g) \right| + \left| \text{rank}_i^{\text{human}}(y_b) - \text{rank}_i^{\text{pred}}(y_b) \right| \right]
\]

with a score closer to 1 indicating stronger alignment with human preferences.

**Prob. prefer longer:** This metric measures the likelihood that the model’s preferred response is longer than the alternative, indicating a potential length bias in the model’s preferences.

We evaluated the performance of two models, Gemma-2-9B-It and Llama-3.1-8B-Instruct, with the results presented in Table 9. These results show that the MCQ-based calibration achieves the highest alignment with human preferences while exhibiting a relatively lower bias toward longer responses.

<table>
  <tr>
    <th rowspan="2">Method</th>
    <th colspan="2">Agreement (%)</th>
    <th rowspan="2">Prob. prefer longer (%)</th>
  </tr>
  <tr>
    <th>GEMMA-2</th>
    <th>LLAMA-3</th>
  </tr>
  <tr>
    <td>MCQ</td>
    <td>68.21</td>
    <td>65</td>
    <td></td>
  </tr>
  <tr>
    <td>P(True)</td>
    <td>61.27</td>
    <td>52</td>
    <td></td>
  </tr>
  <tr>
    <td>P(True) w/ ref.</td>
    <td>67.75</td>
    <td>76</td>
    <td></td>
  </tr>
  <tr>
    <td>MCQ</td>
    <td>65.59</td>
    <td>75</td>
    <td></td>
  </tr>
  <tr>
    <td>P(True)</td>
    <td>63.12</td>
    <td>60</td>
    <td></td>
  </tr>
  <tr>
    <td>P(True) w/ ref.</td>
    <td>65.47</td>
    <td>76</td>
    <td></td>
  </tr>
</table>

Table 9: Alpaca Evaluator Performance Comparison.

We further examined the impact of different reward calibration methods on PAD with PPD Loss using two benchmarks, Alpaca-Eval 2.0 and Arena-Hard. The results, shown in Table 10, demonstrate that the MCQ-based method consistently outperforms other methods, achieving the highest performance on both benchmarks.

<table>
  <tr>
    <th rowspan="2">Method</th>
    <th colspan="2">Alpaca-Eval</th>
    <th colspan="2">Arena-Hard</th>
  </tr>
  <tr>
    <th>LC (%)</th>
    <th>WR (%)</th>
    <th>LC (%)</th>
    <th>WR (%)</th>
  </tr>
  <tr>
    <td>MCQ</td>
    <td><b>49.62</b></td>
    <td>59.50</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>P(True)</td>
    <td>44.09</td>
    <td>53.96</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>P(True) w/ ref.</td>
    <td>48.59</td>
    <td>57.74</td>
    <td></td>
    <td></td>
  </tr>
</table>

Table 10: Impact of Different Reward Calibration Methods on PAD with Gemma-2

These findings highlight the significant impact of the chosen reward calibration method on PAD performance. The MCQ-based method not only aligns more closely with human preferences but also improves the effectiveness of PAD training, leading to better alignment in the distilled model.

Reviewer paper summaries: PAD combines preference learning and knowledge distillation by replacing traditional pairwise preference modeling with a probabilistic distribution, enabling the capture of subtle human preferences. It outperforms both traditional and modern distillation methods across benchmarks with improvements exceeding 20%.

The paper introduces a novel framework for large model distillation, called Preference-Aligned Distillation (PAD). By modeling the teacher model's preference knowledge as a probability distribution, PAD aims to guide the student model more effectively. The proposed pipeline consists of generating diverse responses, computing rewards, calibrating those rewards, and finally training the student model to imitate the teacher model's preference distribution
Summary of strengths: 1. Replaces traditional pairwise preference modeling with a probabilistic distribution to better capture subtle human preferences. 
2. This paper conducts extensive experiments on multiple benchmarks. And shows robust performance and achieves improvements over existing methods.

- The overall PAD pipeline looks well-structured and appears conceptually sound.

- Experimental results demonstrate the effectiveness of the method
Summary of weaknesses: 1. Table 2 shows that increasing the sample size from 4 to 12 raises GPU hours from 12.32 to 52.74. This suggests that PAD requires substantially more computational resources than traditional pairwise preference methods like DPO.

2. PAD requires token-level log-likelihood for reward computation. Closed-source APIs like GPT-4 and Claude do not provide this information. This limitation restricts PAD to open-source models.

Clarity on Contributions: Since I did't have much experience on self-rewarding methods, it is difficult to assess the novelty of the method. Specifically, in Sec 3, it remains unclear whether the proposed Log-Likelihood Rewards are the authors' own contribution or a direct adaptation of prior work. Similarly, the calibration strategy introduced in Sec 4, along with the distillation methodology, seems also be following existing approaches. This uncertainty makes it challenging to evaluate the exact contributions and novelty of the paper

Exposition and Writing: Certain sections of the paper need clearer explanations, like how this work differs from and improves upon related studies. Additionally, the summary in lines 117–124 appears somewhat exaggerated; the paper describes a distillation approach aimed at transferring large-model capabilities to smaller models, yet the discussion in that summary reads more like a new RLHF approach. This mismatch can confuse readers about the paper's primary focus.
Comments, suggestions and typos: I am wondering about the exact resources needed compared with DPO and SimPO.

none
Overall assessment scores: 3

3
Metareview score: 3
Metareview: This paper proposes Preference-Aligned Distillation (PAD), a novel framework for improving preference distillation in the context of aligning large language models (LLMs) with human preferences. While existing methods often rely on pairwise comparisons of responses to determine preferences, they typically overlook the intensity or degree of these preferences. PAD addresses this gap by representing teacher model preferences as a probability distribution over multiple responses, enabling fine-grained modeling of preference intensities.

The framework introduces three key components:

1. High-temperature sampling from the teacher model to generate diverse candidate responses.

2. Reward computation using either external reward models or internal reward signals from the teacher LLM itself, with support for both pairwise and multiple-choice question (MCQ) settings.

3. Preference-Aligned Distillation (PPD) loss, which guides the student model to match the teacher's preference distribution over responses.

The authors also propose using language models themselves as intrinsic reward models for preference evaluation—highlighting an efficient and scalable reward mechanism without needing separate reward model training.

Experimental results across four alignment benchmarks—OpenAssistant, HH-RLHF, MT-Bench, and AlpacaEval—demonstrate that PAD achieves state-of-the-art performance, consistently outperforming strong baselines such as DPO, KTO, and DPO++. Additional results on the Qwen-2.5 model and thorough ablation studies further strengthen the empirical validation of PAD’s effectiveness.
Summary of reasons to publish: There are several compelling reasons to publish this paper at a ACL venue:

Novel Formulation of Preference Distillation: The paper introduces a unique way to model preference signals as full distributions over multiple responses, as opposed to binary pairwise comparisons. This fine-grained modeling contributes to a deeper understanding of preference learning in the context of language model alignment—a central topic in the NLP community today.

Strong Empirical Validation: The method is thoroughly validated on multiple competitive benchmarks, demonstrating consistent and significant improvements over state-of-the-art baselines. The results are robust and generalizable across different tasks and settings.

Relevance to ACL Themes: With the growing interest in aligning LLMs with human intent and feedback, PAD directly addresses challenges in preference modeling, safety, and efficient fine-tuning—making it highly relevant to researchers in NLP and beyond.