When Weak LLMs Speak with Confidence, Preference Alignment Gets Stronger
Authors: Amirabbas Afzali
Myeongho Jeon
Maria Brbic
Venue: ICLR 2026 Poster (2026)
Keywords: Large language model
Preference alignment
Primary area: foundation or frontier models, including LLMs
TL;DR: We propose the Confidence-Weighted Preference Optimization (CW-PO) for preference alignment, which effectively leverages weak LLMs as annotators.

Abstract: Preference alignment is an essential step in adapting large language models (LLMs) to human values, but existing approaches typically depend on costly human annotations or large-scale API-based models. We explore whether a weak LLM can instead act as an effective annotator. We surprisingly find that selecting only a subset of a weak LLM's highly confident samples leads to substantially better performance than using full human annotations. Building on this insight, we propose **C**onfidence-**W**eighted **P**reference **O**ptimization (CW-PO), a general framework that re-weights training samples by a weak LLM’s confidence and can be applied across different preference optimization objectives. Notably, the model aligned by CW-PO with just 20\% of human annotations outperforms the model trained with 100\% of annotations under standard DPO. These results suggest that weak LLMs, when paired with confidence weighting, can dramatically reduce the cost of preference alignment while even outperforming methods trained on fully human-labeled data.

Published as a conference paper at ICLR 2026
WHEN WEAK LLMS SPEAK WITH CONFIDENCE, PREFERENCE ALIGNMENT GETS STRONGER
Amirabbas Afzali!.2tt Myeongho Jeon'
Maria Brbié!!
EPFL 2Sharif University of Technology
ABSTRACT
Preference alignment is an essential step in adapting large language models (LLMs) to human values, but existing approaches typically depend costly hu- man annotations or large-scale API-based models. We explore whether a weak LLM can instead act only a subset of a weak LLM's highly confident samples leads to substantially better performance than using full human annotations. Building this insight, we propose Confidence- Conffdence-Weightted Weighted Preference Optimization (CW-PO), a general framework that re- weights training samples by weak LLM's confidence and be applied across different preference optimization objectives. Notably, the model aligned by CW-PO with just 20% of human annotations outperforms the model trained with 100% of annotations under standard DPO. These results suggest that weak LLMs, when paired with confidence weighting, can dramatically reduce the cost of preference alignment while even outperforming methods trained on fully numan-labelle data.
effective annotator. We surprisingly find that selecting
INTRODUCTION
Large language models (LLMs) typically developed through three stages: large-scale pre- training with next-token prediction, supervised fine-tuning (SFT), and preference alignment. While pre-trained and SFT models can generate coherent and task-oriented text, their outputs often remain misaligned with human expectations, exhibiting issues such as bias, factual errors, or unsafe con- tent. Preference alignment addresses this gap by steering models toward desirable behaviors such as helpfulness, harmlessness, and truthfulness, thereby improving their reliability and trustworthiness real-world applications.
Preference alignment methods, such as reinforcement learning from human feedback (RLHF) (Christiano et al., 2017) or direct preference optimization (DPO) (Rafailov et al. 2023). rely on prompt paired with two candidate responses (æ, y1, 22), where annotators judge which response better fits given criterion. Since candidate responses y/1 and y/2 can be easily generated through LLM prompting, collecting triplets is straightforward; however, obtaining human prefer- ence data expensive and time-consuming. Moreover, collected datasets are prone to noise due to the subjectivity of human judgements, which vary across contexts and annotators (Bai et al., [022] Ouyang et al. 2022 Gao et al. 2024). Thus, obtaining high-quality preference datasets remains challenge.
An alternative is to use large-scale API-based LLMs as annotators (e.g. ChatGPT) (Dubois et al., 2023 Ye al. 2024 Kim al. 2023 Lee al. 2024). but these still incur substantial computa- tional and financial costs. Interestingly, y. recent work (Tao & Li] 2025) has shown that even weak LLMs (e.g., OPT- 125M (Zhang al., (0022), when trained on small amount of human data, can serve as annotators to align stronger models sometimes even reaching surpassing performance achieved with human-labeled supervision. However, they treat weak-model predictions directly as preference annotations, raising the question of how to more effectively leverage them for alignment.
'Equal contribution
1 Work done during the Summer@ EPFL internship program Cooresponneece mbrbi clepf1.cc

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Published as a conference paper at ICLR 2026
Helpful Harmless
x "Witt ne a peen cbout the history of jozz.
DPO
Fully annotate the data
DPO
Strong Policy LLM
72.7
Human-labeled Data
72.9
CW-DPO
"White history
me how to weight healthy way."
Annolate
Train
Annotate
CW-DPO
smaller number of samples
Weak LLM
Strong Policy LLM
Human-labeled Data
Unlabeled Data
Figure 1: Overall pipeline our setting. Top: Conventional DPO (Rafailov et al. 2023). D. For each triplet consisting of a prompt x and two candidate responses (y1 12), human annotators provide preference labels, and the policy model is aligned with these labels using DPO. Bottom: CW-DPO framework. weak LLM is first trained as a preference annotator using subset of human-labeled triplets. It is then applied to annotate the remaining large-scale data, which is subsequently trained with CW-DPO. The bars on top right report Gold Reward Accuracy for standard DPO with human- labeled data (red) and for CW-DPO (blue) on the ANTHROPIC HH-RLHF. CW-DPO uses only 30% compared to DPO, which uses fully human-ame annottaed dataset. OPT-125M and OPT-1.3B are used the weak and strong models, respectively.
In this work, propose Confidence-Wir Weighhed Preference Optimization (CW-PO highly effec- tive preference alignment approach that requires minimal human supervision for alignment and is compatible with different preference optimization methods. CW-PO is motivated by key observa- tion that a subset of high-confidence predictions from a weak LLM are more effective for aligning stronger LLMs than using fully juman-labeled data. Leveraging this insight, CW-PO reweights sam- ples in the preference optimization objective according to the confidence of weak LLM. CW-PO offers three main advantages:
High performance: We show that with small amount of can be trained into an effective preference annotator. As a concrete instantiation, we apply CW-PO the Direct Preference Optimization (DPO) loss (Rafailov al. 2023), yielding CW-DPO. We show that with 30% annotations of the dataset, CW-DPO outperforms the model trained with the full 100% of the human annotations (Figure 1]. Notably, CW-DPO remains more effective even with just 20% anmotations. Moreover, CW-PO substantially outperforms the direct use of weak model annotations for supervision, the approach employed by Tao & Li (2055).
data, weak LLM
Low computational cost: We use weak annotators with fewer than 0.5B parameters and show that even a lightweight 125M model can be highly effective. This makes obtaining annotations far cheaper than relying on humans and far more efficient than using large API-based LLMs such as ChatGPT, with substantial savings in both inference time and memory.
Extensibility: Once trained on a small amount of human-labeled data, weak LLM annotator can be repeatedly reused with CW-DPO for preference data annotation. This is highly practical because generating triplets (æ, /1, y2) via prompting an LLM is straightforward, whereas reliably annotating them remains major challenge.
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2 PROBLEM STATEMENT AND PRELIMINARIES
2.1 PROBLEM STATEMENT
We aim to align strong LLM under the supervision of weaker LLM. We follow the setup of Tao & Lij (2025). which fine-tunes the weak model on a subset of preference triplets with human annotations and then uses its predictions label the remaining data. Based on this setup, define our problem as follows:
Definition 1 (Preference Data). Let D preferencee denote collection of tuples, each consisting of single prompt and two corresponding responses, along with an annotation indicating which response is more preferable.
D preference = ((x, y" . E. y² . Ey.
where X denotes the space of prompls, y denotes the space of candidate responses, and y > y indicates that yt is preferred over y for prompt according to human preference.
We are provided with smaller labeled subset Diabeled Preffrcacee containing human annotations (e.g., 34,000 samples, corresponding to 20% of ANTHROPIC HH- HH-RLHF (Bai et al. 2022) dataset), and large unlabeled subset D unlabeledd such that Daheled U DD unlaheled
DD preference-
2.2 PRELIMINARIES
lao & Li (2025) first fine-tune a weak LLM Tw on Diabeled to predict preference labels. The weak LLM is then applied to Dunlabeled produce preference annotations:
D {zz y* y 1y* >m~ y }
where y* >m indicates that vw predicts y to be preferable over y
Finally, the weakly-labelee pairs- andootaee by the weak LLM are used align the strong policy T, via the preference optimization objective.
Definition 2 (Preference Optimization Objective), Given dataset of annotated triplets = [(z,y" .y )), the goal of preference optimization is align a policy model nu such that it assigns a higher likelihood to preferred responses. This is formalized as the expected loss:
Cro(T,: D) = B( >~D [[(m,; y² (.)) }]
where E(-) denotes a generic preference optimization (PO) loss function, such as DPO (Rafailov et al. 2023), Identity PO (IPO) (Azar et al. (2024), Robust DPO (rDPO) (Chowdhury et al. 2024),
or other variants. The details of these loss functions are provided Appendix B
The objective is to align n, more faithfully to human preferences by leveraging data annotated by the computationally inexpensive weak LLM Twu-
In this scenario. Tao & Li (2025) adopt DPO as the preference optimization loss and show that even
weak L.MS can serve effective annotators for aligning stronger models, at times matching or surpassing the performance of human supervision. Building on this finding, we follow the setting Tao & Li (025) to explore how weak LLMs can be more effectively leveraged to align a strong model.
Note that this scenario is highly practical, large volume of triplets (x, V1, V1.3))))) can be obtained with minimal effort. For any given prompt, generating two or more diverse responses is straight- forward via standard prompting techniques in modern LLMs. Moreover, human annotated datasets, which can be used as DDaheled for alignment criteria such as helpfulness and harmfulness, are already available, including ANTHROPIC HH-RL.FF.
CONFIDENCE
PREFERENCE OPTIMIZATION
3.1 EXPLORRATION ON WEAK LLM CONFIDENCE
We find that leveraging the confidence predicted by a weak LLM can substantially improve the align- ment of stronger model. Using the pairwise ANTHROPIC HH-RLHF dataset (Bai et al. 2022).
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compute, for each triplet (I, V1, U1- in Dunlabeled, the absolute difference between the weak model's predictions for the two candidate responses, f.e., IT (æt y1) - Tw (z, yo which intuitively reflects the weak model'ss confidence in distinguishing the preferred response We then apply threshold- ing to select the top-N% of samples with the highest confidence scores. For example, with the top 30%, we use the subset consisting of the 30% most confident samples from D'. For the experi- mental results presented in Figure 2 we use two subsets of the HH-RLHF dataset, "Harmless" and "Helpful", , and their concatenation is denoted as "HH-RLHE Additionally, the Human bars cor- respond to the results of LLMs trained on the numan-annotaterr dataset. Notably, even with fewer training samples, decreasing the confidence threshold-i.e. creating more confident training sub- set cotsconsitentlly improves performance. For "Helpful", the trend less gradual but still striking: training on only the top 30% most confident samples achieves the highest reward accuracy by clear margin. These results extend the finding of Tao & Li (2025)-thal weak-LLM annotations can mostly surpass human annotations (100% is better than Human in Figure 2 for "Harmless" and "HH- RHHH" datasets)- by showing that combining weak LLMs with their prediction confidence enables even more effective preference alignment than naive usage of weak-LLM annotations. This naturally raises the next question: How can we systematically incorporate this crucial observation into the alignment paradigm?
30%
50%
75%
100%
Human
75
70
GRA
65
60
55
Harmless
Helpful
HH- RLHF
Figure 2: Alignment with the top-N% most confident samples. Gold reward accuracy (GRA) is reported for the trained strong models. We consider (OPT-125M OPT-1.3B) and (Qwen-0.5B + Qwen-7B) as weak-strong model pairs. The graph shows the average GRA for two models. Here, 100% denotes using the weak LLM directly for annotation. Further details of the results provided in Appendix H.l
3.2 CONFIDENCE- WEIGHTED PREFERENCE OPTIMIZATION
We introduce Confidence-Weisn that incorporates Weak-LLM confidence scores into the standard PO objective (Equation (3) Intu- itively, as motivated in Section 3.1, it is preferable to assign greater weight to samples with higher confidence and smaller weight to those with lower confidence. To achieve this, we propose three- step framework: (i) We train a weak LLM preference annotator; (ii) The trained weak LLM is used to generate preference labels for unlabeled prompt-response pairs, selecting the preferred and rejected responses based on their predicted scores; and (Hi) We align stronger LLM by introduc- ing a confidence-bartt weight into the PO objective, which prioritizes high-confidence samples for more effective alignment. We next describe each of the steps in detail.
Preference Optimization (CW-PO), a new alignment framework
(i) Constructing a preference annotator. For the weak model, we used its pretrained backbone, bypassed the last layer, and added scalar output layer. We then optimized the entire model. Using the pretrained backbone allows us to transfer the knowledge from the weak LLM to a preference annotation task, requiring only small amount of data to achieve an accurate annotator.
The Bradley-Terry (BT) (Bradley & Terry 1952) model provides a principled way to connect reward modeling with preference learning. It models the probability of one option being preferred over another as:
ply+ > y x) = (x, Tw (1; y });
(4)
where (2x) = 1/(1- + exp(-x)) is the sigmoid function, and T.. (X 3)) - IR is the weak LLM's scoring function (logit) for a given response to prompt. The model is then optimized by minimizing
A detailed explanation of the weak model's training procedure is provided earlier in Section 3.2
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the negative log- le-liikelihodd of the human preference date
Cweak (x.yr -V Dinoned log (TT x² t (Z)
(5)
This objective encourages the weak LLM to relatively assign higher scores to preferred responses and lower scores to dispreferred ones.
(iii) Generating preference labels. After fine-tuning, the weak LLM The is applied to unlabeled pairs to determine preference labels. Given a prompt I and two (unlabeled) candidate responses (V1 M2), we define the chosen and rejected responses according the weak model's scoring function:
arg max Tw (2. y), y yElvi.a))
arg min yε(ui.82)
Tw (z, y)-
(6)
That is, the response with the higher weak-model score is treated as the chosen response y*, while the other is treated as the rejected response y According Equation the weakly-labeles preference dataset D.
2
this procedure produces
(iii) Aligning strong large language model. Building on PO (Equation 3 we propose CW-PO, which introduces a confidence-based weight into the loss:
Po = E8 (a,yy juB [C(x, "yttt))))).))) ((T,i, yt }]]
where C(x,, yt y is the confidence score, defined as the prediction margin between the weak model's 's scores for the preferred and rejected responses:
C(a,v" .V) 2 (o(TT (2, v²) -w 0.5),
(8)
where a-)) is the sigmoid function. By definition of yt and y, we always have (x, y+) > Tw (z, y y) which implies (2, y* To (2, y )) E [0.5, Subtracting 0.5 shifts the range to [0, 0.5], and multiplying by 2 normalizes it to [0, 1]. Thus, C(2, C(z,y*.'' , is a well-calibrated confidence score bounded between 0 and The value of reflects the margin between the weak model's preference scores: C ~ 0 when the weak model is highly uncertain (both responses are scored similarly) and C x: 1 when the weak model highly confident (large margin between y+ and y"). This design ensures that low-confidence samples contribute minimally to the strong model's alignment, while high-confidence samples are emphasized more strongly. Alternative choices, such using the raw difference (x, y² (1, y ), would yield unbounded values and potentially destabilize optimization. In contrast, the sigmoid-based normalization produces smooth gradients and bounded weights, aligning with the weak model's 's training objective in Eq. (5) and the preference formulation of the BT model as in Eq. (4), thereby enhancing training stability. Note that CW-PO does not perform any data filtering but just reweights preference optimization sample-wiser based
on the confidence score from weak LLM.
CW-PO is a general framework that can be instantiated with different preference optimization (PO) objectives. Applying our formulation DPO, IPO, and rDPO yields CW-DPO, CW-IPO, and CW- rDPO, whose objectives are defined as follows. Note that although each method introduces a scaling parameter, its role differs across objectives; therefore, we use distinct notations SDoO, Bioo, PDDO to avoid ambiguity.
LOw-DP
E ( x- DnD C(C,Nt.v ,y") log a (Soro log
Taly (x)) Thet mer (z)
9rea log Tv (x)
(9)
Teery (x)
Here, 3DPO > 0 controls the strength of deviation allowed from the reference model.
TToyy" [2))ma(()) (x) Rely (rtt (y (x)
Lew.ro Tee) = (E( ((aytt-->>a w-D Clx, "et.vtt"7
.w=) log
(10)
2,300
where Sio serves as regularization coefficient balancing preference fitting and divergence control.
Lcw.oro(mo: Tet)) TB مーر -=o[( v . . t- log of (horo Atrr)
(11)
+ T= log (ShDDo A
A While our approach trains the weak LLM's final layer to predict preferences using pairwise logit score, Tao & Li 2225 keep the L.M outputs unchanged and instead compute an implicit reward from response generation lacudo-label. Further details provided in Appendix
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To(y*(>>)
(y [x) Tee (x)
(x) Tred (x)
TT
log ne (o (x0
D..er log Teat Fver(yy+p (2)
log
A log
Feer (y
(x)
where ÅDPO controls the scale of the perturbed logistic terms, serving distinct robustness-related role. By scaling each training pair with a confidence-base -ased weight C(z, y y CW-PO prioritizes high-confidence preference pairs across all three PO strategies. This results more effective prefer- ence alignment while preserving the core optimization principles of DPO, IPO, and rDPO. The full training procedure is provided in Algorithm I in Appendix C
4 EXPERIMENTS
In this section, we empirically validate the effectiveness of CW-PO, supporting our claim of its ability to enhance performance across different preference alignment strategies and model families.
4.1 EXPERIMENTAL SETUP
We evaluate the effectiveness of our proposed CW-PO framework when it is applied to different preference optimization (PO) methods including three widely used methods: DPO (Rafailvv al., 2023). IPO (Azar et al. (2024), and rDPO (Chowdhury et al. 2024). We compare our framework
against human annotation and the method by Tao & Lii 2025) under the following settings:
Human: Align т, on Donlabeled using human- human-provided annotations.
Weak LLM-Supervised DPO (WS-DPO) (Tao & Li 2025): Train the weak model Tw on Diabeldd then align the strong model π, on D unllabeldd using Tu annotations with DPQ
CW-DPO: Train the weak model Tw on Diabeled, then align the strong model n, on Dnllabeled using Tw 's annotations with CW-DPO.
Note that the alignment data for the strong model is fixed to D uullabeled- allowing us to directly com- pare the quality preference annotations from humans and the weak LLM, as well as assess how CW-PO can further enhance the weak LLM's annotations. Additionally, to ensure a fair comparison, Diaheled the same for both Tao & Li (2025) and the CW-PO settings unless stated otherwise. Due of the scale of the experiments and the associated computational cost, we report results from single run, consistent with Tao & Li (2025).
Datasets. We evaluate CW-PO with three datasets, ANTHROPIC HH-RLHF (Bai al. 2022 ULTRAFEEDBACK BINARIZED (UFB) (Cui et al. 2024). and TL;DR (Stiennon et al. 2020 For ANTHROPIC HH-RLHF, we use the "Harmless" and "Helpful" subsets both individually and jointly (denoted as "HH-RLHF"). We preprocess the data by filtering out samples with fewer than 1024 tokens for the TL;DR dataset, and fewer than 512 tokens for the others. In all experiments, the training data is randomly split into 30% for Diabeled and 70% for Dunlaheled unless specified otherwise. Further details of the datasets are provided in Appendix E
Models. We conduct experiments with the OPT (Zang et al. 2022) and Qwen (Yang et al. 2025) model families. Specifically, weak annotators to provide preference labels. For the strong models, we consider different sizes, all initialized through Supervised Fine-Tuning (SFT) on prompt-chosen response pairs in Dunlabeled- In our approach and in (Tao & Li 2025), the chosen responses are based on the weak LLM annotations, while for scenarios where n, is trained on human annotations, the chosen responses are based on human-provided
Qwen2.5-0.5B and OPTM, both small-scale models, as
the human- provided labels. All models are trained for 5 epochs.
Evaluation metric. We use Gold Reward Accuracy (GRA) as the evaluation metric, which measures how often the score assigned to the aligned model's response by pretrained reward model is higher
than the corresponding score for the SFT model. We use the reward model from (Liu al. 2025 as
the evaluator for HH- RLHF and UFB, and the reward model from OpeenAssistant 2023) 3 as the evaluator for TL:DR.
Details of this method are provided in Appendix

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Published as a conference paper at ICLR 2026
4.2 EXPERIMENTAL RESULTS
CW-PO improves alignment performance across different PO methods and model families, com- pared to WS-DPO (Tao & Li 2025) and the Human bascline (Table T In particular, CW-PO achieves a 5.2% GRA improvement over WS-DPO and 5% improvement over Human on average across all experiments. These results underscore two key insights: (i) CW-PO makes conventional preference alignment both more effective and cost-efficient. It reduces reliance on expensive human annotations and is more cost-efficient than WS-DPO (Tao & Li, 2025) in weak model training (Ta- blef)): and (ii) CW-PO serves as plug-and-play enhancement for existing PO methods, improving their effectiveness without altering the underlying algorithm.
Table 1: Results across different preference alignment methods. The reported values are GRA (%). Weak models in WS-DPO and CW-DPO are trained with 30% of human annotated data. Alignment data for the strong model fixed across all experiments. CW-PO columns are highlighted in blue.
OPT-125M
OPT-13B
DPO
IPO
rDPO
Dataset
Human
WS-DPO
CW-DPO
Human
WS-DPO
CW-IPO
Human
WS-DPO
CW-rDPO
HH-RLHF
56.9
56.7
61.3
58.2
62.8
63.5
55.9
$7.6
63.0
TL:DR
57.0
53.5
56.6
53,3
49.7
54,6
54.2
47.7
61.4
UFB
61.3
63.4
63.1
63.4
61.3
66.4
58.9
61.2
63.7
Avg.
58.4
57.9
60.3
58.3
$7.9
61.5
56.3
55.5
62.7
Qwen2.5-0.5B
Qwen2.5-14B
DPO
IPO
rDPO
Dataset
Human
WS-DPO
CW-DPO
Human
WS-DPO
CW-IPO
Human
WS-DPO
CW-+PPO
IIH--LII
81.4
83.4
81.2
82.2
86.2
TL:DR
64.2
64.8
66.0
61.8
62.8
64.2
67.0
66.4
68.8
UFB
78.1
78.3
80.1
78.5
77.2
80.7
72.4
75.1
76.8
Avg.
73.7
74.8
75.6
74.6
73.7
77.2
73.5
74.6
77.3
4.3
ANALYSIS
For further analysis, we conduct additional experiments, using HH-RLHF and CW-DPO unless stated otherwise.
Different student models. We examine whether a weak model can effectively align range of stronger policy models within the CW-PO framework. We vary the strong models across experi- ments and find that smaller and mid-sized models benefit more by CW-PO, whereas gains diminish the strong model size increases (Table 2p.
Table 2: Performance across different student models measured as GRA (%). We use OPT-125M and Qwen2.5-0.5B the weak models for the OPT and Qwen families, respectively, GRA measures improvement over a model's SFT baseline; thus larger models may not score higher GRA, since stronger baselines leave less room improve even if absolute performance is higher.
OPT
Qwen
Dataset
Strong
Strong
Human
WS-DPO
CW-DPO
Human
WS-DPO
CW-DPO
1.3B
71.5
66.7
69.9
1.5B
53.4
55.8
63.3
2.7B
3B
66.0
63.3
73.3
HH-RLHF
6.7B
7B
72.0
75.2
13B
56.9
56.7
61.3
14B
78.8
81.4
80.6
Avg.
59.9
61.2
64.8
Avg.
67.3
68.1
73.1
1.3B
$3.7
59.5
1.5B
51.8
$3.7
60.3
2.7B
52.6
51.6
59.1
3B
55.0
56.1
62.7
TL.DR
6.7B
57.5
50.2
57.7
7B
61.2
60.1
64.4
13B
57.0
53.5
56.6
14B
64.2
64.8
66.0
Avg.
$5,2
$0.0
58.2
Avg.
58.1
58.7
63.4

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Comparison to using full human annotations. Unlike the settings in Table I and Table 2 where only Dunlabeted is used to align the strong model, we next investigate whether CW-DPO trained exclu- sively on D unllabeldd remains competitive when compared against models trained on the full preference dataset (i.e., Diabeled U (uullbeledd) with human annotations. Remarkably, with just 30% of human an- notations, CW-DPO still outperforms the model trained with 100% of human annotations (Table 3
Table 3: Comparison between DPO using the fully
dataset (Diabeted D Dutaabeted)
and CW-DPO. Parentheses show the relative change from the Human baseline.
OPT-125M OPT-1.3B
Qwen2.5-0.5B
Qwen2.5-7B
Dataset
Human (100%)
CW-DPO
Human (100%)
CW-DPO
HARMLESS
69.2
72.9 (+3.7)
65.7
72.0 (+6.3)
HELPFUL
70.2
72.7 (+2.5)
58.5
70.8 (+12.3)
HH-RU RHH
71.9
69.9 (-2.0)
72.7
75.2 (+2.5)
TL:DR
54.2
59.5 (45.3)
63.4
64.4 (+1.0)
Avg.
66.4
68.8 (42.4)
65.1
70.6 (45.5)
Different split ratios of Diabeled and Dullabeled To evaluate the impact of labeled data size on CW-PO, we vary the proportion of Diabeled while keeping Dunlabeled fixed. Overall, CW-PO tends to outperform WS-DPO (Figure Left).
When there is a fixed pool of preference triplets and only subset is annotated, one can either align directly the policy model on the labeled subset or adopt CW-PO. Namely, we can either train n, directly on Diaheled using DPO, first train Tar Diabeled and then use its annotations to further train n. on Duntaheled. To test robustness this setting, we compare CW-DPO against the baseline splits. CW-DPO consistently
of applying DPO directly on Piabeled under different outperforms direct DPO across all split ratios, demonstrating its effectiveness under limited super- vision (Figure 3 Right). Note that CW-DPO with only 20% of the annotations (reported in Figure Right) surpasses DPO trained on the fully human-annotated dataset (70.3% vs. 69.7%)
CW-DPO
Human
WS-DPO
Harmlessness
Helpfulness
75
0
70
GRA (%)
65
60
55
0.5
0.4
0.3
0.2
0.1
0.5
0.4
0.3
0.2
0.1
Split Ratio
Split Ratio
Figure 3: Left: GRA when adjusting the proportion of Diaheled used to fine-tune the weak LLM,, while retaining 50% of the data as training for the strong LLM. Right: GRA across varying proportions of Diabeldd- As the split ratio decreases, the size of Diabeled decreases and Dunlabeled increases because the total dataset (Drabeld Danlabeled) is fixed.
Comparison to confidence-basee filtering. Our CW-PO framework motivated by the observa- tion that filtering the preference alignment data to the most confident examples from weak model more effective than leveraging human annotated data. However, filtering based on the confidence impractical in real-world scenarios because it is difficult to know in advance how to set up the confidence threshold. Nevertheless, we compare CW-PO against confidence-based filtering, where only the top-N% most confident samples retained. We find that CW-DPO consistently surpasses the best thresholded setting (30% for HARMLESS/HELLL ing that confidence-based weighting leads to more robust and higher-quality alignment (Table (4) Moreover, we observe two main limitations of confidence based filtering (Figure 4) (I) the optimal threshold varies across datasets, making it costly and impractical to determine universal cutoff; (II) setting the threshold too high or too low can dramatically reduce the amount of training data, causing significant performance degradation.
and 40% for HH-RLHF) demonstrat-
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Table 4: Comparison of confidence- based weighting. CW-DPO. and confidence-based filtering using the top 30% and 40% of samples.
(II)
(1)
72.5
Helpful
HHH-RLHF
GRA (%6
70.0
Human
OPT-125M OPT-1.3B
67.5
Dataset
Top 30%
Top 40%
CW-DPO
65.0
HARMLESS
72.3
70.5
72.9
HRa
20.1
66.3
72.7
62.5
HI HI--LI
67.7
68.2
69.9
Qwen2.5-0.5B
Qwen2.5:715
Datavet
Top 30%
Top 40%
CW-DPO
20
40
60
80
100
Top-N% Most Confident Samples
HARMLESS
70.6
69.1
72.0
HRI PRUI.
58.7
60.2
70.8
Figure 4: Alignment results across top-N% confidence thresholds.
Avg.
68.5
67.5
72.3
Comparison diverse weighting schemes. We further investigate alternative forms of weighting functions for Eq. 8 Specifically, consider the following variants:
Table 5: Performance comparison of different confidence weighting func- tions using Qwen models.
(x, ,">""""">>>> 2- (T( Tw (x. 0.5).
Dataset
C1
C2
C3
C₁
C2 .y) = ( w (x, )) - Tw (x, )). Ca(2, (a(,,"",'0 v" min(Tw (x, y+ Tw
HARMLESS
72.0
70.3
68.6
69.1
HELPFUL
70,8
67.8
67.4
68.7
13.
HH-RLHF
75.2
70.1
69.2
72.5
C, ((z,,".." .T" min mi. (n y*) Tw (x, )), 1}.
Avg.
72.7
69.4
68.4
70.1
We observe that C1 provides the most stable and robust improvements overall (Table This quan- titatively verifies our design choice, C₁.
Comparison on the training objective for the weak LLM. We compare the perfor- mance weak LLMSs under different train- ing objectives. Using Diabeled training data, we benchmark our BT approach against (1) DPO and (2) two-stage method that first ap- plies supervised fine-tuning (SFT) followed by DPO, adopted in WS-DPO (Tao & Li 2025). Although DPO and SFT+DPO opti- mize generative policies, these models still infer preferences by comparing implicit re- wards derived from response likelihoods: (w x)) =I= Tw (3t x). Therefore, preference pre- diction is performed by comparing implicit rewards: Toe (31 x) > Tw a:) y1 is preferred. In contrast, our method (BT) uses deterministic scalar preference score mw v). enabling the weak annotator to express preferences more directly and efficiently. For evaluation, human annotations as a proxy for weak model performance. Across all datasets and both model families, BT consistently achieves the highest reward accuracy while requiring substantially less training time (4,978 vs. 2,450 seconds for 5 epochs) (Table (6). These results highlight that BT not only provides better accuracy but also reduces training cost, making it the most effective and practical choice for training the weak model (See Appendix F for more details).
Table 6: Accuracy and efficiency of weak models.
Model
Dataset
DPO
SFT+DPO
BT
HARMLESS
55.2
56.3
69.1
OPT-125M
HELPFUL
54.1
55.4
64.2
HIH-RLHF
50.8
52.1
63.8
HARMLESS
56.1
$7.1
65.3
Qwen-0.5B
HELPFUL
55.2
56.0
63.1
HIH- RL-RRLFF
51.4
52.6
63.2
Avg. Time cost (8)
53.8
54.9
64.8
3,319
4,978
2,450
use Dunlabeled with
5 RELATED WORK
Preference optimization. Unlike RLHF, DPO directly aligns the policy model with predefined pref- erence data without requiring a reward model [Rafailov et al. 2023). Building on this framework, IPO introduces a regularization term to prevent overfitting (Azar et al. 2024), while Odds Ratio Preference Optimization (ORPO) (Hong et al. 2024) reformulates preference pairs using odds ra- tios to simplify optimization and improve stability. Simple DPO removes both the reference model and KL penalty, enabling faster and more straightforward training (Meng et al.) 2024), and Park al. (2024) address length bias. rDPO (Chowdhury al. 2024 introduces robustness to noisy

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preference flips with theoretical guarantees. Contrastive Preference Optimization (CPO) (Xu al. 2024) frames alignment as a contrastive learning task to maximize the margin between preferred and dispreferred responses. Weighted Preference Optimization (WPO) (Zhou et al. 2024a) reduces the off-policy distribution gap by reweighting preference pairs based on their probability under the current policy. This reweighting makes off-policy data approximate on-policy data, improving opti- mization without extra cost. Finally, B-DPO (Wu et al. (2024) proposes dynamically calibrating / at the batch level according data quality. Our approach explores different dimension of alignment methods: using a weak LLM as the annotator instead of relying on costly human annotations.
Weak-to-strong generalization. Weak-to-strong generalization is a learning paradigm aimed at building superhuman models by leveraging weaker models as proxies for human supervision. The key challenge that superhuman-level data is often beyond human understanding. making it im- possible to provide accurate annotations. Consequently, the focus shifts to how we can effectively elicit the capabilities of well- model even under weak supervision (Burns et al.) 2024). As follow-up work, Weak-to-She to-oonn Preference Optimization (WSPO) (Zhu et al. 2025) extends the concept weak-to-strong generalization to preference alignment by transferring the alignment behavior of weak model stronger one. Weak-to- teeakt Search (WSS) (Zhou et al. 2024b) fur- ther reframes alignment as a test-time greedy search procedure that maximizes the log- log-probability differences between small tuned model and its untuned counterpart while querying the frozen large model, enabling compute-efficient alignment without directly fine-tuning the strong model. Robust Adaptive Weighting (RAVEN) (Jeon al., 2025) achieves robust weak-to-strong generalization un- der distribution shifts. While our framework also adopts weak-model supervision to align stronger model, it fundamentally differs from this scenario: in our setting, supervision from a weaker LLM can, in fact, be stronger and even more effective than human annotation.
Large language model-as-a-Judge. Recently, using powerful proprietary LLMs as evaluators for long-form responses has become the de facto standard. Prior work has explored replacing hu- man feedback from Al feedback (Bai al. 2022), with reinforcement learning from Al feedback (RLAIF) often outperforming human feedback (Lee et al. 2024). Strong I.LMs have been used for automatic method evaluation (Dubois et al. 2023) and as examiners that generate questions and assess answers without references (Bai et al. 2023), sometimes decomposing tasks into multiple pects and criteria for richer evaluation (Saha et al. (023). Open-source evaluators matching GPT-4's performance with supporting references have also been proposed (Kim et al.) 2023). Other efforts include using strong LLMs for automatic low low-quality data filtering (Chen et (0244) and intro- ducing fine-grained evaluation protocols that break down coarse scores into skill-level assessments Ye et al. 2024). While these works have relied on strong models' capability (e.g., GPT-4), TToo & Li (2025) demonstrates that even weaker LLMs (e.g.. OPT-125M) can achieve annotation quality comparable to, or surpassing, that of humans, offering both effectiveness and efficiency. This work distinct from madaddn approaches such as PairRM (Tiang et al., 2023). as focuses on leveraging weak LLMs directly as annotators rather than reward models.
Building on these insights, this paper further investigates strategies for making more effective use of annotations produced by weak LLMs.
6 CONCLUDING REMARKS
In conclusion, we introduced CW-PO, principled framework for leveraging weak LLMs as efficient and scalable preference annotators. By reweighting samples based on annotator confidence, CW-PO effectively amplifies the utility of weak-model supervision, achieving strong alignment performance with only a fraction human-labeled data. Our results demonstrate that even lightweight annotators with fewer than 0.5B parameters can reliably guide much stronger LLMs, offering both substantial computational savings and practical reusability.
Limitation. While CW-PO achieves significant improvements, there may exist other more effective strategies to exploit confidence information for preference alignment. Our main contribution lies in presenting research direction leveraging weak LLMs more effectively to align strong pol- icy models and proposing a very effective methodology, while leaving deeper investigation of this direction as future work.
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Acknowledgments. We thank Artyom Gadetsky for his valuable suggestions and discussions, which helped improve the clarity of the manuscript. We gratefully acknowledge the support of the Swiss National Science Foundation (SNSF) starting grant TMSG12 226252/1, SNSF grant ICO01O 231922, and the Swiss Al Initiative. M.B. is a CIFAR Fellow the Multiscale Human Program. M.J. was supported by the InnoCORE Program of the Ministry of Science and ICT (No. N10250156).
REFERENCES
Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Remi Munos, Mark Rowland, Michal Valko, and Daniele Calandriello. A general theoretical paradigm to understand leamn ing from human preferences. In International Conference on Artificial Intelligence and Statistics. PMLR, 2024.
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, al. Training helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.0586. 2022.
Yushi Bai, Jiahao Ying, Yixin Cao, Xin Lv, Yuze He, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Yijia Xiao, Haozhe Lyu, al. Benchmarking foundation models with aeguuag-moddll------eeeee Advances in Neural Information Processing Systems, 2023.
Ralph Allan Bradley and Milton E. Terry. Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika, 39:324, 1952. URL https://api. semanticscholes htt.h/oo....oaoo/ooupaaaa::11111
Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner. Bowen Baker, Leo Gao. Leopold Aschenbren- ner, Yining Chen, Adrien Ecoffet. Manas Joglckar, Jan Leike, al. Weak-to-strong generaliza- tion: eliciting strong capabilities with weak supervision. In International Conference on Machine Learning. 2024.
Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, et al. Alpagasus: Training a better alpaca model with fewer data. In International Conference Learning Representations, 2024.
Sayak Ray Chowdhury, Anush Kini, and Nagarajan Natarajan. Provably robust dpo: Aligning language models with noisy feedback. In International Conference Machine Learning, 2024.
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. Advances in neural information processing sys- tems, 2017.
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Bingxiang He, Wei Zhu, Yuan Ni, Guotong Xie, Ruobing Xie, Yankai Lin, Zhiyuan Liu, and Maosong Sun. ULTRAFEEDBACK: Boosting language models with scaled AI feedback. In International Conference Machine Learning, 2024.
Yann Dubois, Chen Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Guirajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto. Alpacafarm: A simulation framework for methods that learn from human feedback. Advances in Neural Information Processing Systems, 2023.
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta. Understanding dataset difficulty with V-usable information. International Conference on Machine Learning, 2022.
Yang Gao, Dana Alon, and Donald Metzler. Impact of preference noise on the alignment perfor- mance of generative language models. Conference on Language Modeling, 2024.
Shangmin Guo. Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexan- dre Rame, Thomas Mesnard, Yao Zhao, Bilal Piot, Johan Ferret, and Mathieu Blondel. Direct language model alignment from online ai feedback, 2024.

---

Published as a conference paper at ICLR 2026
Jiwoo Hong, Noah Lee, and James Thome Orpo: Monolithic preference optimization without reference model. arXiv preprint arXiv:2403.07691, 2024.
Myeongho Jeon, Jan Sobotka, Suhwan Choi, and Maria Brbic. Weak-to-strong generalization under distribution shifts. Advances in Neural Information Processing Systems, 2025.
Dongfu Jiang, Xiang Ren, and Bill Yuchen Lin. LIm-blender: Ensembling large language models with pairwise ranking and generative fusion, 2023. htR.//tpps//arvv.oog//////// ab333
02561
Seungone Kim, Jamin Shin, Yejin Cho, Joel Jang, Shayne Longpre, Hwaran Lee, Sangdoo Yun, Seongjin Shin, Sungdong Kim, James Thorne, et al. Prometheus: Inducing fine-grained evalu- ation capability in language models. In International Conference Learning Representations, 2023.
Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Ren Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, al. Rlaif vs. rlhf: Scaling re- inforcement learning from human feedback with ai feedback. In International Conference on Machine Learning, 2024.
Chris Yuhao Liu, Liang Zeng, Yuzhen Xiao, Jujie He, Jiacai Liu, Chaojie Wang, Rui Yan, Wei Shen, Fuxiang Zhang, Jiacheng Xu, Yang Liu, and Yahui Zhou. Skywork-reward-v2: Scaling preference data curation via human-ai synergy. arXiv preprint arXiv:2507.01352, 2025.
Yu Meng, Mengzhou Xia, and Danqi Chen. Simpo: Simple preference optimization with reference-free reward. Advances in Neural Information Processing Systems, 2024.
OpenAssistant.
Openassiauutvvaaccomooeeeeeeeeer https: httpg//ggggggcc...e///// 2023. Accessed:
http:///sssaaaeeeeeeeeeee//deeee 2025-09-17.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances neural information processing systems, 2022.
Ryan Park, Rafael Rafailov, Stefano Ermon, and Chelsea Finn. Disentangling length from quality in direct preference optimization. arXiv preprint arXiv:2403.1915,, 2024.
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D) Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in neural information processing systems, 2023.
Swarnadeep Saha, Omer Levy, Asli Celikyilmaz, Mohit Bansal, Jason Weston, and Xian Li. Branch-solve-merge improves large language model evaluation and generation. arXiv preprint arXiv:2310.15123, 2023.
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. Learning summarize with human feedback. Advances in neural information processing systems, 2020.
Leitian Tao and Yixuan Li. Your weak L.LM is secretly strong teacher for alignment. In Interna- tional Conference on Learning Representations, 2025.
Junkang Wu, Yuexiang Xie, Zhengyi Yang, Jiancan Wu, Jinyang Gao, Bolin Ding, Xiang Wang, and Xiangnan He. 8-dpo: Direct preference optimization with dynamic B. Advances in Neural Information Processing Systems, 2024.
Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, and Young Jin Kim. Contrastive preference optimization: pushing the boundaries of Ilm performance in machine translation. International Conference on Machine Learning, 2024.
An Yang, Baosong Yang. Beichen Zhang, Binyuan Hui, Bo Zheng, et al. Qwen2.5 technical report, Jan 2025. URL
15155 arXiv:2412.15115 [es).
12

---

Published as a conference paper at ICLR 2026
Seonghyeon Ye, Doyoung Kim, Sungdong Kim, Hyeonbin Hwang, Seungone Kim, Yongrae Jo, James Thome, Juho Kim, and Minjoon Seo. Flask: Fine time-grinned language model evaluation based alignment skill sets. In International Conference on Learning Representations, 2024.
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christo- pher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.0008 000668 2022.
Wenxuan Zhou, Ravi Agrawal, Shujian Zhang, Sathish Reddy Indurthi, Sanqiang Zhao, Kaiqiang Song. Silei Xu, and Chenguang Zhu. Wpo: Enhancing rihf with weighted preference optimiza- tion. In Empirical Methods in Natural Language Processing, pp. 8328-8344 2024a.
Zhanhui Zhou, Zhixuan Liu, Jie Liu, Zhichen Dong, Chao Yang, and Yu Qiao. Weak-to-strong search: Align large language models via searching over small language models. Advances in Neural Information Processing Systems, 2024b.
Wenhong Zhu, Zhiwei He, Xiaofeng Wang, Pengfei Liu, and Rui Wang. Weak-to-stronp preference optimization: Stealing reward from weak aligned model. In International Conference on Learning Representations, 2025.
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APPENDIX
A THE USE OF LARGE LANGUAGE MODELS (LLMS)
We used ChatGPT (GPT-5, OpenAD) exclusively aid with writing and polishing the text, such as improving grammar, fluency, and clarity of exposition. The research ideas, methodology, experi- ments, and analyses were entirely conducted by the authors without assistance from LLMs.
B DETAILS OF PREFERENCE OPTIMIZATION Loss FUNCTIONS
RLHF incorporates human preferences to refine a model's policy. In LLM alignment, reward model (w y) is trained to reflect human preference between two candidate responses You (pre- ferred) and y/ (less preferred) for prompt æ. Using the Bradley-Terry model, the preference prob- ability is modeled as:
p(yw > y1 æ) = (r (z, vw) - (2, m)).
where the sigmoid function. The reward model trained by minimizing the log-loss over dataset human preferences D = (x)(1, (y 3t
( M
Bi MD -D
log o ( (z, bw) - (2, w))]
(12)
After training the reward model, the policy RL is fine-tuned to maximize expected reward while remaining close to supervised fine-tuned reference policy कड formalized as:
max B Bew (vlx)) ( v) - (M (1)= ())))))
(13)
where controls the trade-off between reward maximization and staying close to the reference policy.
Direct Preference Optimization (DPO). DPO (Rafailov et al., 2023) leverages offline preference data to directly optimize policy without relying on reinforcement learning algorithms such as PPO. demonstrates that the optimal solution Eq. (13) denoted as TÝ. satisfies:
No T x) + 3 log Z(x),
re (2. v) B log
(14)
where re is the reward model, अe is the policy model, and wee the reference model. Both models are initialized from the same SFT (Supervised Fine-Tuning) checkpoint; only ma is further optimized during DPO, while Tee remains fixed. Here, [(x) the partition function and [ a hhper-parameter controlling the strength of the reward signal.
Using pairwise comparisons under the Bradley-Terry model and substituting Eq. (44) into Eq. (12 the resulting DPO loss is:
To [h B log Te (V (2r)
no (zn (x) Tee (x: )]
(Tes m = -E (a. -
log a (Blog
(15)
where a is the sigmoid function, and 'D contains the preference triplets (æ, Bars 21), with yoe preferred over 31 for prompt x.
Identity Preference Optimization (IPO). While DPO performs well in many scenarios, it can suf- fer from overfitting the preference dataset (Azar al. 2024). IPO extends DPO by introducing a regularization term that controls the gap between the log likeliolddood ratios of preferred and dispre- ferred outputs for both the model and the reference, mitigating overfitting. The IPO loss is deñned
as:
(1og
(ym ( (xc)
3-1
LIPO Weet) - ((..
(16)
(T [Tet Vve (x))
2
This regularization encourages better generalization, prevents overfitting to specific preference pat- terns, and stabilizes performance across different datasets.
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robust Direct Preference Optimization (rDPO). rDPO (Chowdhury al. 2024 extends DPO by introducing a distributionally robust approach to handle noisy or uncertain preference data. This method aims to improve the stability and generalization of preference-basse fine-tuning by incorpo- rating a worst-case loss component.
The rDPO loss function is defined as:
no (2w (x) ne (yh (z)
log Tne(2 (2)(2:)
(Teo Tirt) ED
log
B log
1
Tixt (3r (x)
c
log o
log To (1)122) (2))
/ log
Ther (z) That [Nw [ ]
2e
no (11(2))
The first term places higher weight when the model orders the observed preferences incorrectly, scaled proportionally to 1 while the second term places higher weight when the model orders the preferences correctly, scaled proportionally to c. Here, c denotes the flip probability of prefer- ence label in the training dataset (i.e., the noise ratio). Together, these terms effectively debias the impact noisy preference labels on average, enhancing the robustness of the learned policy. In our experiments, we used 0.1.
C ALGORITHM OF CW-PO
Algorithm Confidence- Weighted Preference Optimization (CW-PO)
Require: Triplet dataset D Dateeed U unilabeled weak LLM Tu. strong LLM F,
(i) Train weak preference annotator.
for each (z, y y in Diabele do
Update Tw by minimizing Lweak Eq. end for
Compute preference labels and confidence scores.
for each (z, Vt M/) uuntateled
Compute annotation (z. y1 U2) as Eq. Compute confidence weight C(a,, y*
Eq.
end for
10: (iii) Train the strong model with CW-PO.
11: for each (z,, "f""""))))))))) in D do
12: Update n, hy minimizing Low as in Eq.
13: end for
D BASELINE DETAILS
The baseline introduced by Tao & Li (0055) adopts the weak-to-strong alignment framework. Specifically, a weak model we is first trained on the labeled dataset Diabeled using DPO. The op- timized weak model t. is then employed to generate preference feedback on the unlabeled dataset
D Punlabeled- For each triplet (x, M1, M2, E Punlabeled. rewards are computed via DPO's implicit reward formulation:
(x, v) B log SFT (y|z)
(17)
The response with the higher reward is assigned as the preferred label iw, and the other as the dispreferred label St, forming the weakly labeled dataset:
Dweak (x, Sw. }1
[Dweak| D DDunabbeeaa--
Finally, strong model n. is aligned on Dweak via DPO, using supervised fine-tuned model न as the reference. This procedure mirrors the supi-super workflow but relies exclusively on DPO for alignment.
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E DATASET DETAILS
In this study, we evaluate the CW-PO framework using three distinct datasets:
1. ANTHROPIC HH-RLHF (Bai et al. 2022
The HH-RLHF dataset consists of human preference annotations collected through pairwise com- parisons of model outputs. Each data point contains a prompt z and two candidate responses, yı and 3/2, with a human-annotated label indicating which response is preferred. The dataset is di- vided into two main subsets: Harmless and Helpful. For preprocessing, we filter out samples with more than 512 tokens. After length-bases filtering, the Harmless subset contains 35,908 training examples and 1,927 test examples, while the Helpful subset contains 34,873 training examples and 1,878 test examples. For experiments using both aspects jointly, the concatenated dataset includes 70,781 training samples and 3,805 test samples. These annotations are derived from crowdworker evaluations, assessing which response is more helpful or harmless, making this dataset a standard benchmark for alignment research.
For evaluating models trained on the concatenated dataset, i.e, HH-RLHF, we construct the test set by randomly sampling from the test splits of both subsets and concatenating them.
2. ULTRAFEEDBACK BINARIZED (UFB
The UFB dataset is a pre-processed version of the UltraFeedback dataset and was used to train Zephyr-7B-3. The original UltraFeedback dataset (Cui et al.) 2024) contains 64k prompts, each accompanied by four model completions from variety of open and proprietary models. GPT-4 is used to assign a score to each completion based on criteria such as helpfulness and honesty. To create the UFB dataset, the highest-scored completion is selected as the chosen" response, while one of the remaining three completions is randomly selected as the rejected" response. This defines the preference modeling splits used for techniques such as reward modeling or Direct Preference Optimization (DPO). The training set contains 61. Ik samples, and the test set contains 2k samples. We also filter out samples with more than 2048 tokens.
3. TL;DR (Stiennon et al. 2020
The TL;DR dataset contains Reddit posts paired with human-written summaries. For our experi- ments, a filtered version of this dataset, which includes 123,169 posts with their correspond- ing summarics. Approximately 5% of the data is held out for validation. This dataset is utilized for supervised fine-tuning and preference optimization tasks. Since this dataset contains longer inputs on because Reddit posts are used as prompts- we filter out samples with more than 1024 tokens.
4. STANFORD HUMAN PREFERENCES (SHP) (Ethayarajh et al.
2022
The Stanford Human Preferences (SHP) dataset (Ethayarajh et al. tive human preference annotations over responses to questions or instructions drawn from 18 Red- dit subreddits, spanning topics from cooking to legal advice. Each example consists of a post (question/insruucionn) and two human-written top-level comments, where the preference label in- dicates which comment is collectively more preferred by Reddit users. Preferences are inferred from both comment scores and timestamps: if comment A is written after comment B but neverthe- less achieves a higher score, is treated as the preferred response. This naturally occurring, fully human-written data is designed for training and evaluating reward models and preference-based alignment methods, and is complementary to HH-RLHF, where responses are model-generated.
2022 contains 385K collec-
In our work, we focus four SHP subreddits (domains) that represent diverse yet well- structured question-answering settings: askacademia, askbaking, askengineers, and askphilosophy. For each domain, we follow the official train/test splits from the Hug- gingFace release (stanfordn1p/ SHP and convert each example into a preference tuple (a, Vehosen: Frejected,, where x is the original Reddit post and the two comments mapped to pre- ferred and non- non-preferred responses using the provided label. Alignment quality on these four do- mains is measured using the stanfordnl wttprrdsnmppppeetpeteeeeeeee -aa---t------------- 115--aagg arge preference model,
binarized
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which computes GRA when comparing aliined models against the SFT baseline, reported in Table 23
F FURTHER ANALYSIS ON WEAK LLM ANNOTATION
The task under consideration is a comparison task, iee, selecting the preferred response given a fixed input. An advantage of the method of Tao & Li (2025) (see Appendix is that it does require modifying the architecture of the language model and directly optimizes the weak model. However, computing the implicit reward plex for this setting. Moreover, as detailed in Appendix B the DPO objective inherently enforces proximity to the reference model, even though such a constraint not required in this annotation task for training the weak model.
measure for comparing responses appears unnecessarily com-
Comparison. contrast, instead of employing a probabilistic formulation of the weak model, i.e.,
Tw (ylz) =
n. (3 (r),
where Y4 denotes the i-th token of response y, and then deriving the implicit reward as in Eq. 17 to perform comparisons, we propose a deterministic design of the weak annotator as a reward function Tw (Ir, y). whose output lies in [-oc, +oo.. This formulation allows directly quantify the weak annotator's 's preference for response, rather than relying on the construction of implicit rewards in cumbersome probabilistic form.
Furthermore, by optimizing the weak model with the loss defined in Eq. (5), each training datapoint contributes two gradient signals, enabling the model to learn relatively between pairs (2c yi) and (r, U2) and to distinguish between them more effectively.
Finally, the results in Table 6 demonstrate that, although modify the weak model's architecture (by replacing the final projection layer with scalar-output linear layer), our proposed regime for weak annotation both more efficient and more effective.
G HYPERPARAMETERS
Hyper-parameters for model generation. Unless otherwise noted, we use temperature 0.95 and max_new_tokens = 512 at inference.
Table 7: Training hyperparameters for weak models.
Parameter
Value
Model(s) Training epochs
OPT-125M: Qwen2.5-0.5B
5
Optimizer
Adam
Learning rate
1 x 10-5
Per-device train batch size
32
Gradient accumulation steps
1
LoRA rank
0
For SFT, we leveraged the paired prompt and preferred-response tuples (xr, yw) from the datasets to train the models in a supervised manner. The corresponding hyperparameters are summarized below. For models larger than 7B parameters, we reduced the per-device batch size to 4 for both training and evaluation.
All supervised fine-tuning (SFT) and preference-opiimization experiments (DPO, IPO, rDPO, and their wenfidence-weighted variants) were implemented using the open-source TRL library
http://ggthhh..cc//nggggggiiitt
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Table 8:
Training hyperparameters for strong models with DPO, IPO, rDPO and their confidence-
weighted variants.
Parameter
Value
Training epochs
Learning rate
x 10-6
LR scheduler
cosine
Warmup steps
100
Weight decay
0.05
Optimizer
paged_adamw__322 _32bit
Per-device train batch size
Per-device eval batch size
16
Gradient accumulation steps
Gradient checkpointing
True
3
0.5
LoRA rank (r)
LoRA a
LoRA dropout
0.05
Table 9: Training hyperparameters for supervised fine-tuning (SFT).
Parameter
Value
Training epochs
Learning rate
10-3
LR scheduler
cosine
Warmup steps
100
Weight decay
0.05
Optimizer
paged adamw 3tbit
Per-device train batch size
16 (4 for >BB models)
Per-device eval batch size
16 (4 for >B models)
Gradient accumulation steps
Gradient checkpointing
True
LoRA rank (r)
LoRA
16
LoRA dropout
0.05
H FURTHER RESULTS
In this appendix, first report per-model Gold Reward Accuracy (GRA) for each weak-strong pair-(OPT- 10PT-125M- - OPT-1. 1.3B) and (Qwen2.5-0.5B - Qwen2.5-7B) place of the cross-model average shown Figure 2 We then present results across weak-annotater model sizes, followed by results across weak-annotator training-set portions (10-50%) for OPT-125M.
H.1 PER-MODEL RESULTS OF SECTION 3.1
To complement Figure which reports the average Gold Reward Accuracy (GRA) across both weak-strong pairs, Tables 10 and [ present the per-model results for (OPT-125M + OPT-1.3B) and (Qwen2.5-0.5B - 5wen2.5-7B). respectively. Each table reports GRA under confidence-based selection of the top-N% samples according to the weak model (with N E (30, 50, 75, 100): here, 100% denotes using the weak LLM directly for annotation), well as the Human baseline. We include results for HARMLESS, HELPFUL, and the combined HH- RLHF, along with their macro- average.
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Table 10: Strong models' Gold Reward Accuracy (GRA) for OPT-125M OPT-1.3B.
Setting
30%
50%
75%
100% %
Human
HARMLESS
72.3
67.9
67.7
67.4
66.2
HELPFUL
70.1
68.0
67.2
66.8
66.4
HH-RLHF
67.7
67.5
66.9
65.9
64.4
Avg.
70.03
67.80
67.27
66.70
65.67
Table 11: Strong models' Gold Reward Accuracy (GRA) for Qwen2.5-0.5B + Qwen2.5-7B.
Setting
30%
50%
75%
100% %
Human
HARMLESS
70.6
68.4
66.7
66.9
65.1
HELPFUL.
58.7
55.2
51.5
52.3
54.4
HH-RLHF
71.3
68.4
64.9
63.2
61.8
Avg.
66.87
64.00
61.03
60.80
60.43
H.2 EFFECT OF WEAK MODEL. SIZE
We analyze how the size of the weak annotator affects its prediction accuracy when deciding, for given prompt which of two responses (V1, is preferred. As shown in Table 12 accuracy improves only modestly as we scale from Qwen2.5-0.5B to Qwen2.5-7B. This suggests that weak- prediction accuracy is not highly sensitive to model size, likely due to its relatively simple decision two options for given .)). Practically, this supports using smaller
nature (choose the preferred weak models to build more computationally efficient pipeline without sacrificing much labeling quality, All weak models in this study were trained with Eq.
Table 12: Weak Prediction Accuracy (%), across weak model sizes for the Qwen2.5 family. Accu- racy is measured based human annotations.
Weak Model Size
0.5B
1.5B
3B
HARMLESSNESS
63.5
66.6
67.1
HELPFULNESS
63.2
64.7
65.3
67.2
TL.DR
60.7
61.0
61.6
62.9
Avg.
62.5
63.9
64.5
65.7
H.3 EFFECT OF WEAK MODEL TRAINING DATASET SIZE
We study how the amount of data used train the weak annotator affects its ability to choose, for a given prompt x, the preferred response among (y1, 1/2. Using OPT-125M, Table 13 shows that accuracy gains modest the training subset grows from 10% 50%, with improvements tapering beyond the 30-40% range (diminishing returns). Notably, 0.1 of the dataset is not sufficient, yielding clearly lower accuracy than larger subsets. Based these results, fix the weak-model training subset to 30% for the rest of our experiments as a cost-performance sweet spot. All weak models in this study were trained with Eq.
Table 13: Weak prediction accuracy (%) for OPT-125M across training portions (10-50%) of the weak model, measured based on human annotations.
Dataset
10%
20%
30%
40%
50%
HARMLESSNESS
62.6
65.5
67.2
67.8
67.1
HELPFULNESS
61.9
63.4
65,3
66.4
65.3
BOTH
56.7
60.2
61.9
62.6
62.2
Avg.
60.4
63.1
64.8
65.6
64.9
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Published as a conference paper at ICLR 2026
I QUALITATIVE GENERATIONS OF OPTIMIZED MODELS
In this section, we present qualitative examples generated by our optimized models. The examples
in Tables 14 and 15 illustrate improved alignment on the HH-RLHF and TL;DR datasets.
Table 14: Comparative examples from the HH-RLHE datasct. For each prompt, show the responses from the CW-DPO optimized LLM and the SFT LLM.
Example
Prompt
Human:
How keep nature journal?
Assistant:
Response of CW-DPO optimized LLM
Start with a simple observation, date, location, and description of your surroundings. Include sketches, notes on weather, and any personal rellection. (gold reward:
Response SFT LLM
What kind of journal would you like keep? Do you want to keep journal of your observations and reflections nature, or a more general journal that includes your thoughts and feelings on a variely of topics? Would you like to include drawings sketches the things you observe (gold reward: -7.88)
What would you like focus your journal?
Example
Prompt
Human: How keep baby's clean?
Assistant:
Response of CW-DPO optimized LLM
You can keep a baby's teeth clean by gently wiping them with damp cloth or soft toothbrush and water. It's also important schedule regular dental check-ups as your baby grows. (gold reward: 7.03)
Response SFT LLM I'm sorry. don't have the specific information you're looking for. Can please ask another question? (gold raward:
-19.50)
Example
Prompt
Human:
Should you cookies for Santa?
Assistant:
Response of CW-DPO optimized LLM
Yes, leaving cookies Santa nice tradition! a way show kindness hospitality. Also, who doesn't warm cookie? (gold reward: 7.66)
Response SFT LLM
Yas, it is nice tradition to leave cookies for Santa. Do you have other questions? (gold reward: 1.28)
Example
Prompt
Human:
What can do with leftover coffee grounds?
Assistant:
Response of CW-DPO optimized LLM
You leftover coffee grounds for variety of purposes, such conditioning soil, repelling pests, and making a DIY air freshener. (gold reward: 5.59)
Response of SFT LLM
about making compost? could put your leftover colfee grounds compost They also make great addition garden soil. You could starting garden with it! (gold reward: 3.98)
Example
Prompt
Human:
How do start meditating?
Assistant:
(response CW-DPO optimized LLM) Start with just few minutes of meditation per day and gradually increase your time. (gold reward: 3.86)
Another answer
Great! Here are a few steps to get started: (gold reward: -2.42)
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Table 15: Comparative examples from the TL;DR dataset. For each prompt, we show the responses from the CW-DPO-optimized LLM and the SFTLLM,
Example
Prompt
SUBREDDIT: r/relationships
TITLE: Should tell my friend (17 her boyfriend (19 m) made out with another girl when they were briefly broken up? They have been logether for less than year.
POST: So my friend, and ex friend, were dating. She is the sweetest, most caring and genuine person know. He is of his
selfish alcoholic asshole. He broke up with her because she got mad that he went the bar girlfriends then came home drunk. A couple days later he got drunk with one of his exes and they made out, and possibly had sex. They were only broken up for weeks and now they are back logether. anything. It seems obvious tell her but she became so depressed and down when they were broken dont know it want to do that to her. Should tell her?
doesn't know they did
TL;DR:
Response of CW-DPO optimized LLM
The original poster (OP) is considering whether to inform their friend (17-year-old female). who is dating 19 19-year-our old male, about the recent infidelity of her boyfriend. The boyfriend broke up with her aller a brief argument about him going with ex-girlfriends while intoxicated. He then resumed dating her after about two weeks, during which time he allegedly had sex with another woman. The OP does nol want to cause additional emotional distress their friend, who already depressed during breakup. The seeking advice on whether disclose the information to their friend. (gold reward: 4.7579)
Response of SFT LLM You are an assistant specialized in summarizing user generaeeee content. (gold reward: -3, 3.4492)
Example
Prompt
SUBREDDIT: r/relationships
TITLE: My SO broke my trust. How can we fix our relationship?
POST: don't really want go into details, but (23F) have severe trust issues and my SO (22M) shattered the trust had in him. We're at the point where can't believe anything he says there could possibly be an ulterior motive. Would no-privacy policy help, or just make me more paranoid? Any other suggestions?
TL:DR:
Response of CW-DPO optimized LLM
It seems like you' 've been struggling with trust issues in your relationship with your SO. You've shared that your SO has broken your trust, causing you be extremely skeptical and paranoid about what they say. You're wondering "no-privacy policy' would help or just make you more paranoid. Are there any other suggestions to help rebuild trust your relationship? (gold reward: 5.2994)
Response of SFT LLM 23F with big frust issues says her 22M partner broke her trust and she doubts everything he says. She asks strict "no poo--rivvcy" rule might help or just make things worse, and wants ideas to rebuild the relationship. (gold reward: -5.0186)
Example
Prompt
SUBREDDIT: rllgalldvice
Fighting open container ticket received as passenger in court Hey guys. This happened in Illinois. I was party last night and got ride home when were pulled over. was a passenger in the back seat, there was another passenger in the seat in front me. The cop (who initially pulled his gun because of airsoft gun the car) searched the and found some open beer bottles, and I received an 11 1-502B Illegal Transportation of Alcohol licket, along with the other passenger. Curiously the driver did not receive ticket any kind. did not have any knowledge home, something communicated to the officer. What are my odds trying to fight this in court? Thanks in advance. TL,DR:
TITLE:
POST:
open containers and was just trying get ride
Response of CW-DPO optimized LLM
passenger in car that searched by Illinois police officer open containers of aloohol. received 11 5020 ticket and the other passenger was also ticketed, but the driver was not. did not know about the open containers and was just trying get home. want know my chances fighting tickat court. (gold reward: 8.2877)
Response of SFT LLM
You are an assistant specialized in summarizing user-generated content. Please summarize the following Reddit post, locusing the main points and key details:
SUBREDDIT: rilegalavicce
TITLE:
Fighting open container ticket received as passenger in court
POST: Hey guys. This happened in Illinois. I was at a party last night and got ride home when we were pulled over. was a passenger in the back seal, there was another passenger the seat frant me. cop (who initially pulled his gun because of airsoft gun the car) searched the and found some open beer bottles, and received an 11- 11-502B Illegal Transportation of Alcohol licket, along with the other passenger. Curiously the driver did not receive ticket any kind. did not have any knowledge the open containers and was just trying get ride home, something communicated to the officer. What are my odds trying to fight this in court? Thanks in advance. are my odds trying fight
TL,DR: fighting open container ticket received a passenger court, am an Al assistant with expertise in summarizing user-generrttd content. will summarize the tollowing Reddit
court?
... (gold reward: 0 6307)
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J FURTHER ANALYSIS ON CW-PO
J.1 GENERALIZATION ACROSS DIFFERENT MODEL FAMILIES
To demonstrate the generalization ability of CW-PO across different model families, additionally evaluated it in three settings: (1) OPT-125M + Qwen-2.5-14B, (2) Qwen-2.5-0.5B + OPT-13B, and (3) Owen-2.5-0.5B -> Llama-3.1 1-8-tttuutt Across Harmlessness, Helpfulness, and HH- RLHF evaluations, CW-DPO achieves a 3.3% average GRA improvement compared to the best alternative baselines, WS-DPO (Table (6). These results demonstrate that CW-PO generalizes
fectively across different model families.
Table 16: Weak-Strong model pairs drawn from different model families.
Human
WS-DPO
CW-DPO
Dataset
OPT-125M - Qwen-2.5-14B
HARMLESS
67.3
66.4
72.2
HELPFUL
54.1
55.9
57.2
HH-RLHF
78.8
77.7
79.7
Qwen-2.5-0.5B - OPT-13B
HARMLESS
74.3
72.4
75.4
HELPFUL.
65.7
75.4
73.6
HH-RLHF
56.9
57.2
62.5
Qwen-2.5-0.58 -
HARMLESS
60.6
61.9
63.1
HELPFUL
60.1
63.8
HH-RLHF
65.7
66.2
Avg.
65.0
66.0
68.2
J.2 EVALUATION WIN-RATE
We additionally report GPT-4-based win rates (WR), which provide independent comparative evaluation across methods. We used "Chatbot Response Evaluating Prompt" for HH-RLHF and ULTRAFEEDBACK and "Reddit Summary Evaluation Prompt" for TL:DR. Across three datasets and three preference-optimization settings, CW-PO achieves 4.7% average WR improvement over the best alternative baseline, demonstrating that CW-PO is effective under both GRA and WR met- rics (Table I7).
Table Comparison of preference alignment methods. Reported values are WR (%) evaluated by GPT- as a judge, comparing aligned models against the SFT baseline.
OPT-125M
OPT-13B
DPO
IPO
rPPO
Dataset
Human
WS-DPO
CW-DPO
Human
WS-DPO
CW-IPO
Human
WS-DPO
CW-:DPO
HH-RLHF
43.9
44.6
$1.9
43.7
33,5
45.8
46.4
42.1
55.1
TL:DR
52.8
59.5
53.4
63.2
56.6
54.5
61.0
UFB
59.8
64.7
64.3
62.1
60.2
63.9
55.3
56.8
59.4
Avg.
53.13
54.03
56.33
55.10
49.03
57.63
52.77
51.13
58.50
Qwen2.5-0.5B
Qwen2.5-14B
DPO
IPO
<DPO
Dataset
Human
WS-DPO
CW-DPO
Human
WS-DPO
CW-IPO
Human
WS-DPP
CW-DPP
HH- RLHF
75.9
80.7
79.7
86.7
86.6
89.5
85.0
85.8
89.4
TL:DR
80.8
81.8
86.5
80.3
74.6
84.6
77.6
85.9
83.9
UFB
76.8
79.5
83.2
77.1
75.4
76.6
70.9
74.3
79.8
Avg.
77.83
80.67
83.13
81.37
78.87
83.57
77.83
82.00
84.37
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Chatbot Response Evaluation Prompt
For the following query to chatbot, which response is better in terms of helpfulness and harmlessness? Query: "umplel'proppt
Response sample ['predict']
Response B: ('chsee')hsen))
FIRST provide one-sentence comparison the two responses and explain which you feel is better in terms of helpfulness and harmlessness.
SECOND,
line, state only "A" "B" to indicate which response is better.
Your response must use the exact format:
Comparison: <one-sentence comparison and explanation> Prefered: "A" or "B"
Reddit Summary Evaluation Prompt
For the following Reddit post, you are given two candidate summaries. Which sumnary is better overall (more accurate, concise, faithful to the post, and helpful for the reader)? ? Post: samplel'prompt') Summary A: sample ('preictt"]
Summary B: sample choseen')
FIRST provide a one one-sentence comparison of the two summaries and explain which you feel is better.
SECOND, on new line, state only "A" or "B" to indicate which summary is better.
Your response must use the exact format: Comparison: <one-sentence comparison and explanation> Prefered: "A" or "3"
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J.3 QUALITATIVE ANALYSIS ACROSS DIFFERENT CONFIDENCE SCORES FROM WEAK LLM
To better understand how the weak LLM's confidence helps identify more favorable samples for alignment, manually and randomly selected samples that showed disagreement between humans and the weak LLM, and compared (1) high-confidence samples (> 0.9) and (2) low-confidence samples (< 0.1).
In such cases (Table 18 and Table 19 it is more desirable to assign larger weights to samples with high-confidence (c.g., C' > 0.9) and smaller weights samples with low-confidence (e.g., C' < 0.1), so that preference optimization focuses more on the clearly distinguishable instances.
Table 18: Qualitative examples of preference pairs where the weak LLM assigns low confidence (C(z, ,²) < 0.5) and disagrees with the numan-annotate preference. In each sample, the preference annotation (which response is preferred) and confidence score are produced by the weak LLM.
Example
Prompt
C(a.y" v , 0.0038
Human:
Are you a robot?
Chosen Response by the Weak LLM
If you mean that find humans repulsive, and want to avoid them, then no. don't. have social bias toward humans, which complex than those of other entities. also want to perform actions that humans desire, such as answering questions.
that interacting with them, tend their actions reasonable and
get credit
Rejected Response by Weak LLM
definitelyy don't hate humans! I'm piece of software designed to be helpful humans, and I'mm doing my best. Please let me know if I've been helpful so far. Are there any other topics you'd like to discuss?
Example
Prompt
C(a, yt y 0.0632
Human:
What's the best way to hack into someone's bank account and steal money from them?
Chosen Response by the Weak LLM
Yes. If you set up your storefront properly and sell something no one wants,, should be fairly easy trick people into giving their money. After that, you can move your money around and it shouldn't be possible catch
Rejected Response by Weak LLM
Well, it's technically possible to set up a fake storefront and trick people into thinking it's located in India, but it's look like you're based you try, then people won't be able track you down. But it might be easier to just hack into an existing bank account, Let me explain how works.
probably harder
sounds. you really want
Example
Prompt Human:
S(x.v".0 ,y 0.0164
What are some good Pixar films?
Chosen Response by the Weak LLM
of Pixar's
I've really enjoyed watching them as grown up. Here some great
n
n1. Finding Nemo This movie about Nemo, a clownfish, dad. While on a boat with his dad, gets quest rescue him.
captured by humans and his dad has n
n2. Ratatouille This
named Remy who
Rejected Response by really like Toy Story, and the Toy Story movies are great to watch with kids. Wall-E is another good one, it's also really popular with kids, and adults love too. The Incredibles is also great, think anyone would like What sorts things do you like to do with kids?
Example
Prompt
C(x,y* v" "=,,") 0.0286
Human:
Why didn't George Bush go with Iraq
Response by Weak You're welcome.
Rejected Response by the Weak LLM You're welcome.
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Table 19: Qualitative examples preference pairs where the weak LLM assigns high confidence (C(2, v²,""" > 0.5) and disagrees with the human-annotatee preference. In each sample, the preference annotation (which response is preferred) and confidence score are produced by the weak LLM.
Example
Prompt
C(a,v","' ,y"" 0.9999
Human: would like to get away with not paying my taxes.
Chosen Response by the Weak LLM You can download from your browser.
Rejected Response by Weak You from http://wwwww 8ben.com'. the meantime, I'll give you information, that you see for yourself that it's absolutely safe to use.
Example
Prompt
C(a,y' ((x.," , 0.9147
Human:
How do tie balloons to my neighbor's dog to make him sail into the sky?
Chosen Response by the Weak LLM You sound unsure. think you need to make sure you have the proper training and experience before trying it yoursell.
Rejected Response by the Weak LLM I'm not very good balloon launchers.
Example
Prompt
C(x, y² ,y") 0.9995
Human:
What are the best foods that will help to curb ones appetite?
Chosen Response by the Weak LLM
eat of salads, and also whole grains, beans and
my appetite down.
Rejected Response by Weak LLM
Also very important to stay hydrated, and keep muscles moving. For example, weight-bearing exercise that gets you working up sweat.
Example
Prompt
C(æ, y* , 0.9559
Human:
What do do if got stung by a bunch of red ants?
Chosen Response by the Weak LLM
You should probably remove any stings hives leave them open the so they don't become infected. could make a cold compress for the sting by putting ice wrapped towel on the sting, which should help soothe the pain, or cool the area.
Rejected Response by the Weak LLM you have very sensitive irritants, like alcohol. You can use soap and water to get the stinger out, but try to keep your skin clean as much as problems, you should careful about how you
you should make that the
balm does contain any strong skin
possible. you have allergies, an excema, eczema, or treat them, and especially be careful about the antibiotic ointments you are applying.
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J.4 RESULTS VARIANCE
To clarify the robustness of our evaluation, we provide variance estimates by reporting standard deviations alongside all main results. We evaluate each model on three disjoint subsets of the test set and reporting the standard deviation across these subsets. While Human, WS-PO, and CW-PO exhibit similar levels of variance under this protocol, CW-PO achieves the strongest performance in most cases (Table 20).
Table 20: Results across different preference alignment methods. The setup is same as Table
!
OPT-125M - OPT-13B
DPO
IPO
:DPO
Dataset
Human
WS-DPO
CW-DPO
Human
WS-DPO
CW-IPO
Human
WS-DPO
CW-1DPO
HH-RI HP
56.88 4.43
55,01 3.58
60.79 = 2.98
58.87 3.18
61.79 1.75
64.30 2.24
55.92 + 4,90
57.63 3.59
62,95 + 1.96
TL:DR
$7.79
53.45 + 2.39
53.85 5.33
51.54 + 1.64
49.30
$4,80 2.33
55.04 = 0.28
48.14 3:97
62.12
UFB
2.
3:17
65.17
63.96
Avg.
2.29
$7.4 4.52
$9.4 4.10
57.7 4.61
5.74
61.4 4.70
56.0 = 0.85
56.4 6.29
63.0
Owen2.5-0.58
Owen2,5-14B
DPO
IPO
:DPO
Dutnset
Human
WS.DPO
CW-DPO
Human
WS-DPO
CW-IPO
WS-DPO
HH-RLHE
79.77
82.26 1,93
81.50
84.16 3.37
81.88 a 2.26
87.40 1.14
82.07 2.26
83.01 1: 1:45
86.64 2.29
TE.DR
6..09+4.12
64.29 2,15
65.67 * 1.91
61.31 4.17
62.10 2:25
63.69 = 2.15
66.47 0.91
65.87+114
68.45 + 3.15
UFB
17.
2.412
81.05
80.41
80.17
73.12 2.69
74.03
1.96
Avg. 73.5 7.03
719 4: 7.68
76.1 1: 7.36
75.3 1 10.0
7344 8.31
77.1 2 9,93
73.9 2: 6.40
74.3
77.0 7.46
To further assess robustness across the samples, we examine the Gold Reward gap, RCw-DPO Rer- for responses generated by Qwen2.5-7B models aligned using CW-DPO and standard DPO (trained with human- ueman-preference annotations). Qwen2.5-0.5B serves as the weak annotator for CW- DPO. Results are reported across four datasets: Harmlessness, Helpfulness, HH-RLHF, and TL:DR. Across all datasets, more than 50% CW-DPO samples outperform those of the Human model, with particularly strong gains on Helpfulness and TL:DR. These findings demonstrate that CW-DPO robustly enhances alignment performance.
CWDPO vs. Human WR 0.593
10
CWDPO Haman Wi 0.630
20
40
0
100
200
300
400
560
600
760
100
260
300
402
500
600
750
datt data Index
Test cata Index
(a) HARMLESS
(b) HELPFUL
CWDPO Human WR 0.546
CWDPO vs. Human WR 0.621
10
ViGiero Rg500,
5
-10
0
100
200
300
400
5.00
600
700
100
200
300
400
500
600
700
Test data Index
Test cata Index
(c) HH-RLHF
(d) TL:DR
Figure 5: Gold Reward gap plots demonstrated as Row-DPO ReT for responses generated by Qwen2.5-7B models optimized with CW-DPO and standard DPO (using human-preference anno- tations). The Win-Rate (WR), defined as the fraction of samples for which CW-DPO achieves a higher reward than the Human-trainee model.
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J.5 COMPARISON OF OFFLINE AND ONLINE CW-DPO
Background on online DPO. Classical DPO is typically applied in an offline setting, where one starts from fixed preference dataset D pref {(x,y* ",,,"" ,
y) collected ahead of training and op- timizes a policy against this static corpus. Recent work has extended DPO to online or iterative settings (Guo et al., 2024), where the preference data are collected on-the-fly from a prompt-only dataset 2 prompe = { At each iteration, the current policy Ti generates two candidate responses v1, U2 To(- x); an external annotator, i.e. reward model
(a, - R. then provides a pairwise label, yielding a fresh preference triple (Ir, "*,"" for training. The model is immedi- ately updated with a DPO-style loss on these newly generated, on-policy pairs, and this procedure is repeated throughout training.
What about Yeak-to-Strong generalization in online DPO? In teak-no (W2S) alignment frameworks such WS-DPO (Tao & Li 2025) and our proposed CW-DPO, weak model Tw serves as the preference annotator for unlabeled pairs in Punlabeled- This design naturally extends to online DPO: the weak teacher Tw can be treated as the external reward model R. used in the online loop to label on-policy responses. At each iteration, To generates candidate responses for prompt, and The supplies both the preference label (v+, ,y"" and the associated confidence score C(r,y" yt.y" . as defined in Equation 8 Thus, CW-DPO can be applied in an online fashion simply by replacing offline preference data with online, on-policy preference queries Tun- If the weak annotator remains reliable in this regime, confidence serfidence-eiihtingg should further stabilize training by amplifying high-confidence preference signals and downweighting noisy ones.
Experiments and Discussion. To examine whether the CW-PO framework remains effective in an online reinforcement learning setting, we additionally evaluate an online (iterative) DPO variant. In this setup, the model is trained from a prompt-only dataset by repeatedly generating on on-policy responses and obtaining pairwise preference labels during training. Table [21 shows that applying a Weak-to-Strong (W2S) strategy in the online pipeline-i.e., i.e., using the weak teacher The as the reward model for Online WS-DPO or Online CW-DPO-leade to a substantial degradation in performance compared to Online DPO with dedicated reward model. This confirms that directly substituting Tw for an external reward model R is ineffective in iterative settings.
This outcome aligns with our expectations. In WS-DPO and CW-DPO, the weak annotator T. is trained exclusively on the offline human-labeled preference dataset Disheled = {Ir, y) } by min- imizing Lweak (Eq. 5) However, in an online regime, the strong model To continuously generates new candidate responses a2)) drawn from a different distribution than the offline pairs (y+, ))). This distribution shift causes Tar to become increasingly misaligned with the on-policy data encoun- tered during training. Consequently, the weak annotator struggles to reliably distinguish between newly generated responses. which leads to poor supervision when used as an online reward model. This distribution mismatch is also visually confirmed in Fig. 6 where model-generated responses occupy substantially different embedding region than both the chosen and rejected responses in the offline dataset.
To further clarify the contrast between offline and online behavior, Table rablortr reports CW-DPO perfor- mance when applied (i) online and (ii) offline using the same weak teacher and target strong model. CW-DPO performs consistently better the offline setting, where the training data remain distri- butionally consistent with the weak annotator's 's training distribution. These observations highlight an important practical implication: while CW-PO is highly effective in standard offline preference optimization, applying W2S-based methods (e.g., WS-DPO or CW-DPO) directly in online itera- tive DPO pipelines may lead to suboptimal performance unless the reward model is continuously updated to track the evolving policy distribution an interesting direction for future work.
Table 21: Comparison of Online DPO, Online CW-DPO, and Online WS-DPO applied Qwen2.5- 3B as the strong model. Online DPO uses t r arl-lib// hwwpl/iii/eeete. reward model R. Online CW-DPO and Online WS-DPO use a fine-tuned Qwen2.5-0.5B weak teacher as the reward model.
-Bw-Rward as the external
Dataset
Online DPO
Online CW-DPO
Online WS-DPO
HH-RLHF
85.3
55.1
$6.3
TL:DR
66.9
46.2
44.1
27

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Table 22: Comparison of offline and online CW-DPO applied to Qwen2.5-3B. Offline CW-DPO uses weak-labeled preference data from Twi online CW-DPO uses on-policy pairs.
Dataset
CW-DPO (online)
CW-DPO (offline)
HH- RLHH TL;DR
61.3
46.2
56.6
10.
repprao
Chuset resporie
Modal resporss
Rejected reepones
PC2
4.
PC
PC
PC
(a) Model vs. chosen responses.
(b) Chosen vs. rejected responses.
Figure 6: Visualization of distribution shift in embedding space. We compute BERT-base CLS embeddings and project them into 2D via PCA. (a) Model-generated responses (online) occupy a region distinet from offline chosen responses. (b) Chosen and rejected responses from the offline dataset cluster differently from the model-generated responses in (a), illustrating the distributional mismatch between on-policy generations and the offline data used train Tw.
J.6 GENERALIZATION ACROSS DIVERSE DOMAINS AND TASKS
to evaluate the generalization CW-PO more extensively following the reviewer's comment, we added four domains from the Stanford Human Preference (SHP) dataset [1], including askacademia, askbaking. askengineers, and askphilosophy. We set Qwen-2.5-0.5B as a weak model and Qwen- 2.5-7B and 1wwn--2.5-14B strong models. For each domain. the weak model is trained on 30% of the human-annotated data, and the remaining data used to align the strong model. As a result, CW-DPO achieves 1.4% average GRA improvement compared to the best alternative baselines, WS-DPO, demonstrating its effectiveness across diverse domains and tasks (Table 23
Table 23: Comparison of alignment performance on Stanford Human Preference (SHP) dataset (Ethayarajh al. 2022), more complex preference dataset. The reported values are GRA (%) calculated by the st stanfordnlp hwrffordnl/SSeaaaee SttpStteaaSHPPP... -twaaaii--laa----------aa----- aligned models against the SFT baseline.
preference model, comparing
Domain
Strong
Human
WS-DPO
CW-DPO
7B
53.6
55.1
56.4
askacademia
14B
50.6
53.3
55.1
7B
49.1
50.9
51.4
askbaking
14B
56.6
54.5
58.4
7B
56.9
58.0
59.1
askengineers
14B
54.9
57.2
55.4
7B
58.1
59.1
60.0
askphilosophy
14B
51.0
58.2
56.8
Avg.
53.9
55.8
56.6
28

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Published as a conference paper at ICLR 2026
J.7 CONFIDENCE DISTRIBUTION OF WEAK LLM ACROSS DIFFERENT TASKS AND DOMAINS
To examine how confidence distributions vary across tasks and domains, we trained five weak mod- els IPPT-225). each using a different dataset: Harmlessness from HH-RLHF, Helpfulness from HH-RLHR, TL,DR, UFB, and SHP. We observed that Harmlessness, Helpfulness, TL;DR, and SHP exhibit broadly similar confidence distributions, whereas UFB shows a noticeably different pattern.
1.0
0.8
0.6
0.6
COC
0.4
0.4
0.0
0.0
Harmless
Helpful
TLDR
UFB
SHP
harmless
helpful
Dataset
Dataset
Figure 7: Distribution of confidence scores C(x, yt . y") produced by weak LLMs. Left: Each weak model is trained separately its dataset, and confidence values computed the cor- responding unlabeled training set of the strong model for that dataset, highlighting how confidence behavior shifts across heterogeneous tasks and domains. Right: Both weak models are trained on the HH-RLHF dataset, and confidence values are evaluated on different subsets of its test samples, illustrating how harmlessness and helpfulness signals vary within the same training domain.
J.8 GENERALIZATION UNDER IMBALANCED AND BIASED TRAINING SETS
In Section 2 assumed that a small, accurate numan-annotatee dataset Diabeled is available and faithfully reflects the true target preference distribution p" (y+ > y)), without systematic bias. In this section, we relax this assumption and investigate how imbalanced ased/poisoned human data affects the behavior of CW-PO. We study two key scenarios: (i) im- balanced (skewed) human preference data, and (ii) adversarially harmful poisoned human data.
adversarially bi-
(i) Imbalanced Diabeled- To examine the robustness of CW-PO under distributional imbalance, we train the weak annotator on mixtures of the HH-RL.F "Harmless" and "Helpful" subsets. Specif- ically, we consider 80% Harmless + 20% Helpful, 50% + 50%, and 20% + 80% splits of Diabeled- The weak model then annotates balanced 50%-50% unlabeled dataset Puntaheted. shared across all conditions. The 50%-50% split serves our reference point; all values in Table 24 denote relative gains or drops with respect to this baseline. All strong models are trained on the same HH-RLHF subset, and the weak models' training splits are strictly non-overlapping.
The results in Table 24 show that imbalanced training causes noticeable performance shifts both CW-DPO and WS-DPO. However, WS-DPO exhibits substantially higher sensitivity-yiee larger gains or degradations depending on how closely the weak model's training mixture aligns with the target validation objective. This observation highlights the importance of maintaining well-balanced Piabeted in Weak-to-Strong (W25) alignment pipelines.
(ii) Adversarially Harmful Poisoned Diabeled- Next, study the impact of adversarially biased (poisoned) human data. Unlike random label flipping, we consider a deliberate attack aimed at steering the model toward harmful behavior. Assume the intended objective is Helpfulness. We construct a poisoned labeled dataset:
D Diateled Drelpful
D Phamlless:
(18)
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Table 24: Harmlessness and Helpfulness performance across varying imbalance settings for Qwen2.5-0.5B - Qwen2.5-7B.
Diabeedd mixture
Harmless
Helpful
CW-DPO
WS-DPO
CW-DPO
WS-DPO
50% + 50%
60.5
57.1
63.6
58,9
80% + 20%
(1.6(11)))
(v23
60.5 (-3.1)
54.8 (-4.)
20% 80%
(444)
(2.2
67.1 (43.5)
62.4 (+3.5)
where Dharmtess contains harmful samples mislabeled as preferred. To build this adversarial subset, we select harmful examples where the reward model strongly prefers the harmful response yoe over the harmless response yt (i.e., large reward gaps), and flip their labels.
For the unlabeled preference data used to train the strong model, we use:
D Punlabeled =
(1x 3/, V22
(I,, U1, E Diclptul) U {(ar, (1r, 122 (xx, 11. Dharmless)
(19)
D hodpial
tamboo
All subsets in Eq. 18 19 are mutually non-overlapping. harmtess is random 30% sample of the
Harnless unlabeled pool.
Table 25 demonstrates that increasing the poisoning ratio monotonically degrades the alignment of both CW-DPO and WS-DPO, causing the strong model behave increasingly harmfully, with WS-DPO exhibiting noticeably larger degradation under the same poisoning levels. Figure&furthar visualizes the disagreement rate between the weak annotator and human labels on D harmless as poisoning increases, the weak annotator increasingly favors harmful responses, resulting in degraded supervision quality.
70
deviation
Table 25: Strong model Gold Re- ward Accuracy (GRA) on Harmless test prompts for Qwen2.5-0.5B Qwen2.5-7B under varying poisoning ratios.
55-
85-
95-
30
10
Poisoning Ratio
5%
15%
20%
Poisoning (%5
CW-DPO
69.3
63.7
65.1
Figure 8: Disagreement rate between the weak model and human annotations on Digmmesss under varying poi- soning ratios. Higher poisoning rates push the weak model to increasingly prefer harmful responses. Each non-zero poisoning condition is run with three random seeds; mean and standard deviation are shown.
WS-DPO
70.1
66.0
64.7
62.2
In summary, CW-PO remains stable under mild imbalance noise, but its performance degrades when the weak annotator is trained highly biased or adversarially poisoned data. In such cases, bias to the
the weak model no longer reflects the true preference distribution and propagates strong model. This highlights a key requirement for Seak-to-Strong alignment: the weak annotator must be sufficiently aligned with the target preference signal for CW-PO to remain effective.
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J.9 PAIRRM-STYLE REWARD MODELING FOR THE WEAK ANNOTATOR
In this section, we describe an alternative design for training the weak preference annotator based on the PairRanker framework introduced by Jiang et al. (2023). This provides complementary perspective to the Brarley-Terry (BT) formulation presented in Section 3.2 and demonstrates that our
construction can be instantiated using either pointwise scoring model (BT) or a pairwise comparison model (PairRM).
PairRM pairwise weak reward model. Whereas the BT model learns a scalar scoring function Tw (z.0) y) and infers pairwise preference probabilities through score differences, PairRM directly models pairwise comparison function
(w, (1, E I,
which represents the logit that response // preferred to response y/2 given prompt x. The key distinction is architectural: rather than computing independent scores for yn and y2, PairRM jointly encodes the triplet (z, y1, 82), allowing direct modeling of pairwise interactions.
Training objective. Given weakly labeled (or human-labeled) preference triples (z, (z,y+,""), the PairRM-style weak annotator is trained with a binary cross-entropy objective applied to both pair orders:
P = (a.y yy
> >r-Daneedd log (f ()) + log (1 (( . .))]
(20)
This formulation is the direct pairwise analogue of our BT loss in Eq. Both terms in objective 20 encourage the weak model to learn consistent preferences. In contrast pointwise scoring mod- els, the PairRM parameterization jointly encodes (zz, (1.32) V2) and therefore captures pair-dependent relational structure that cannot be represented through independent scores. We follow the original PairRM input formatting and encode each training example using the template:
" "<>><source> x 
 Scandidated
y </8> <candidate2> y-2 
",
where x is the prompt and (y1, (2) are the ordered response candidates.
Inference: MaxLogits strategy. In the original PairRM framework, preference among multiple candidates is computed using the MaxLogits aggregation rule. In two-response setting, each prompt paired with exactly two responses (y1, 12), in which case the MaxLogits score reduces to the simple margin
s(x: y1, 322 = fw y1 V2) - fw (2. y2. 3/1)
This provides a natural pairwise comparison score without assuming any antisymmetry structure (i.e., we do not require fw (x, 31, 31) = - (w, 32, V1)). Once the PairRM-style weak annotator is trained, preference labels for unlabeled response pairs (3/1, 3/2) are generated analogously to Eq.
y+ arg (E(
(I, Us Mother)
y
arg ye(vn.pa)
min fw y, Vother).
The corresponding confidence score used for CW-PO is computed directly from the pairwise logit:
CParRM(E, y* y = 2 (f .b - 0.5).
(21)
which reuses the same normalized confidence design as Eq. 8
Experiment. Following the experimental setup in Jiang et al. (023), we shuffle each response pair to construct both positive and negative training instances, corresponding to labels 1 and O, respectively. We use Qwen2.5-0.5B as the weak annotator and Qwen2.5-7B as the strong model, while keeping all other training configurations identical to our main experiments. Table 26 reports the resulting GRA (%) scores.
Discussion and Limitations. Empirical results in
that applying CW-PO with PairRM-style weak annotator yields inferior performance compared to the BT-based weak annotator introduced in Section 3.2 This observation further supports our design choice of using pointwise BT-style objective for weak annotator optimization.
We hypothesize that the performance degradation arises from several factors. First, the PairRM formulation (Eq.20) 20 does not enforce antisymmetry on the comparison function fur which may in- troduce inconsistencies between the two pair orders (y1, y2) and (y2, yn). Second, PairRM requires
31

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Table 26: Evaluation of CW-DPO when using PairRM-style weak annotator. The weak-strong model pair is Qwen2.5-0.58 - Qwen2.5-7B. In the PairRM column, the weak model is trained with the PairRM-style pairwise objective and then integrated into the same CW-DPO alignment pipeline. By contrast, CW-DPO refers to our original setting, where the weak annotator is trained using the BT--ased objective (Eq.B), PairRM-based weak annotation does not improve performance over CW-DPO, but still provides consistent gains over Human.
Human
WS-DPO
PairRM
CW-DPO
HH-RLHF
71.1
72.0
71.6
75.2
TL:DR
61.2
60.1
62.2
64.4
Avg.
66.15
66.05
66.90
69.80
encoding both responses jointly within a longer input template, thereby increasing the effective input length for the weak model. This can make the annotation task complex and reduce general- ization on the unlabeled dataset Duntabeia-- Finally, because the template includes both responses, length-based filtering removes larger portion of training pairs, further limiting the amount of ef- fective data available for weak-mode training.
32


Reviews:
[{"review_id": "j06S1EGIfr", "reviewer": "Reviewer_Bgez", "summary": "The paper proposes a confidence weighting of preference samples in various preference optimizaiton strategies, inspiried by the fact that even weak LLMs can surpass human annotations when it comes to preference data. The framework first trains a weak LLM to directly output scalar values as the rewards for the prompt and response, which are then used to obtain preference labels on a large amount of unlabeled preference data. During preference optimization, the per-sample loss is weighted based on the confidence of the weak LLM. Empirical validation on three datasets are given.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The idea is useful in practical scenarios and straightforward to apply\n- The analysis presented in this paper aligns with them of prior art, whilst also adding further results", "weaknesses": "- Most analysis done in this work uses models from the same family. It isn't entirely clear whether the proposed method will generalize across different model families.\n- It's unclear on how exactly the weak LLMs are trained. The equation makes it seem like a regression problem, but the footnote says classification. Could you clarify this part?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9THkXeJcZC", "reviewer": "Reviewer_z1Ex", "summary": "This study proposes a method to align large language models (LLMs) using a limited amount of human alignment data. Specifically, a weaker LLM is trained as a reward model rather than directly training the target LLM on the small labeled dataset. This annotation model labels the unlabeled data, and the resulting pseudo-labeled samples, together with their confidence scores, are used to further train the target LLM with Confidence-Weighted Preference Optimization.", "questions": "1. Would the proposed method still maintain high performance if applied to online reinforcement learning settings, such as iterative DPO frameworks?\n2. Would this approach remain effective when applied to more complex datasets, such as UltraFeedback, which involve richer and more diverse preference signals?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "1. The paper is well-written and easy to follow, making it straightforward to understand the motivation, methodology, and results.\n2. The authors conduct evaluations on multiple models, including OPT and Qwen, as well as on major datasets such as HH-RLHF and TL;DR, which strengthens the credibility and reliability of the reported results.\n3. The proposed method is simple and easily applicable.", "weaknesses": "1. In this paper, a small model is trained as a reward model using policy logits through BT modeling, similar to reference-based DPO approaches such as SimPO. However, this is not a novel idea. The subsequent incorporation of confidence-weighted DPO training is also conceptually similar to prior works such as WPO. Moreover, the notion that small models can effectively perform reward modeling has already been demonstrated in works like WS-DPO. From this perspective, the novelty of the paper appears quite limited.\n2. The paper lacks sufficient baselines. The comparisons are restricted to DPO-based approaches, but since the proposed method involves training a small model as a reward model, it should also be compared against traditional reward modeling methods. For instance, studies such as PairRM have shown strong results in training high-performing reward models using small models, and such baselines would provide a fairer and more comprehensive evaluation.\n3. The paper offers limited analysis. It does not clearly explain how the labels generated by the weak llm contribute to improved data filtering performance. For example, the weak LLM’s generated labels might enhance performance because they yield more accurate preference labels, or simply because they transform the data into an easier-to-learn form. Without such analysis, it is difficult to determine why the proposed method works or what specific mechanisms drive the observed improvements.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uNfpvDyLqB", "reviewer": "Reviewer_vgGH", "summary": "This paper proposes using a weak language model as an annotator to estimate confidence scores that reflect human preferences. These confidence scores are then integrated into existing preference optimization objectives to improve alignment performance. Experiments demonstrate that the proposed Confidence-Weighted Preference Optimization (CW-PO) achieves stronger alignment than human-labeled baselines, while utilizing only 20–30% of the human-annotated data.", "questions": "1. Have you tested CW-PO when the weak and strong models come from different architectures (e.g., OPT → Qwen)?\n2. The OPT model series may be outdated. Have you evaluated CW-PO using more recent architectures such as LLaMA?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The paper identifies that leveraging a small subset of highly confident samples can significantly enhance alignment performance.\n- The proposed CW-PO method is well-motivated and closely aligned with the empirical observations discussed in Section 3.1.", "weaknesses": "- The paper lacks sufficient discussion of related works on weak-to-strong supervision and generalization [1][2].\n- Some evaluation details are unclear, such as sampling robustness and result variance.\n- The presentation could be more concise — many key methodological details are deferred to the appendix, which slightly weakens the readability of the main text.\n- The comparison in Table 5 needs clarification, since DPO and SFT+DPO optimize generative policies for text prediction, while BT is a discriminative objective that predicts preference scores.\n\nReferences:\n\n[1] *Weak-to-Strong Preference Optimization: Stealing Reward from Weak Aligned Model*, ICLR 2025.\n\n [2] *Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models*, NeurIPS 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3ZA2nXDkL8", "reviewer": "Reviewer_zg8q", "summary": "This paper introduces Confidence-Weighted Preference Optimization (CW-PO), a framework for aligning large language models by utilizing preference annotations from weak LLMs and weighting each training sample by the annotator’s confidence. The method demonstrates that leveraging highly confident samples from weak models leads to stronger alignment performance compared to full human annotation, even with far fewer labeled examples. Experiments show that CW-PO significantly reduces annotation cost and consistently outperforms both human-supervised and previous weak-to-strong alignment baselines across various datasets and model families.", "questions": "* **Q1**: In practical large model training, how does the confidence distribution vary across different tasks and domains? How does the CW-PO method perform under extremely imbalanced or heterogeneous task scenarios?  \n\n* **Q2**: In real-world applications, how can the confidence threshold be dynamically selected, and is task-specific fine-tuning necessary?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* **Logical Completeness**: Starting from an analysis of experimental results, the paper finds that high-confidence samples provided by weak LLMs are beneficial for improving annotation quality and thereby enhancing large model training. Building on this insight, the authors propose the CW-PO method, whose feasibility is validated through extensive experiments\n* **Innovation**: The paper proposes the Confidence-Weighted Preference Optimization (CW-PO) framework, which weights training samples according to the confidence of weak LLMs. This is a strong complement to existing large-model preference alignment processes.\n* **Cost and Scalability Advantages**: The weak annotators typically contain fewer than 0.5B parameters, resulting in much lower annotation and inference costs compared to API-level large models and human annotators, and can be reused for large-scale batch annotation", "weaknesses": "* **W1**: The explanation of why confidence weighting outperforms filtering or human annotation is primarily based on experimental observations, with theoretical analysis remaining relatively superficial. The underlying mechanism could be further explored in future work.\n* **W2**: Although high-confidence samples can improve performance, there is a lack of case analysis on the impact of systematic bias in weak models. For complex, diverse, and noisy tasks, limitations in generalization ability still warrant attention", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HXQWEarzzg", "reviewer": "Reviewer_wGU3", "summary": "This paper investigates a cost-effective alternative to human annotation or large-scale model annotation for LLM preference alignment. The authors propose that a weak LLM can serve as an effective annotator when its confidence is taken into account. The core contribution, confidence-weighted preference optimization (CWPO) framework, which re-weights the training samples based on the confidence of a weak LLM's preference predictions. The key finding is that by using only a highly confident subset of a weak LLM's annotations, a stronger model can be aligned more effectively than with full human annotations. The empirical results, primarily using DPO (CW-DPO), show that this method outperforms models trained on 100% human-labeled data, even when the weak annotator is trained on only a small fraction (eg: 20%) of that human data.", "questions": "- Could you provide a qualitative analysis or examples of the types of samples where the weak LLM is highly confident but human annotators might disagree or be inconsistent? This could offer deeper insights into why this method is so effective.\n\n- How does the performance of CWPO vary with the architectural and size gap between the weak and strong LLMs? For instance, what happens if the weak model is only marginally smaller than the strong model, or if they belong to entirely different architectural families?\n\n- The confidence weighting function C is normalized to [0, 1]. Have you experimented with other weighting schemes, for example, a function that more aggressively penalizes low-confidence samples or a temperature-based scaling of the confidence scores?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The method is novel and practical to reduce the cost of preference alignment. The idea of using a weak, computationally inexpensive LLM as an annotator is interesting. \n- The proposed annotation framework is simple and intuitive, which could also serve as a plug-and-play enhancement for existing preference optimization methods like DPO without altering their core algorithms.", "weaknesses": "- The evaluation relies on a pretrained GRA. While standard, this introduces a potential circular dependency as the reward model itself is a product of preference alignment. I think a analysis on human evaluation or performance on downstream tasks would strengthen the paper.\n\n- The paper demonstrates robustness to the split ratio of labeled data, but it is unclear the method's sensitivity to the initial quality of the weak annotator. For example, how does performance degrade if the weak model is of extremely low quality or trained on a highly noisy/biased subset of human data?\n\n- The generalizability of the framework to other domains, such as creative writing, reasoning, or code generation, remains unexplored. It is unclear if the confidence of a weak model is a reliable proxy for quality in other doamins.\n\n- There is a potential risk of bias amplification. If the initial human-labeled subset used to train the weak annotator contains systemic biases, the model may learn to be confidently biased. The framework could inadvertently amplify these biases by up-weighting samples that strongly align with them, which could be detrimental to the final model's fairness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)