Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis

Alven Yang; Dehua Ma; Fuqing Bie; Haoran Wang; Huijia Wu; Jian Zhao; Liuyu Xiang; Yiwei Ru; Zhaofeng He; Zhenbo Xu

REVIEW 4 major objections 5 minor 2 cited by

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis

T0 review · 4 major / 5 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read This paper claims that a single open-source reward model, trained on fully synthetic rubric-grounded preference data, can judge response quality across text, image, video, and audio at state-of-the-art accuracy, replacing scalar, modality-s

desk verdict A genuinely useful rubric-grounded omni-modal reward model with a clean training recipe, but the headline video/audio SOTA claims rest on self-built benchmarks — and the audio one needs direct validation before it can be trusted. read the letter →

arxiv 2602.00846 v2 pith:6FTVDJRX submitted 2026-01-31 cs.CL

Zicheng Kong , Dehua Ma , Zhenbo Xu , Alven Yang , Yiwei Ru , Haoran Wang , Zixuan Zhou , Fuqing Bie

show 4 more authors

Liuyu Xiang Huijia Wu Jian Zhao Zhaofeng He

This is my paper

classification cs.CL

keywords omni-modalrewardmodelrubric-groundedevaluationpreferencesynthesismultimodalalignmentGRPOinterpretablesignalaudiobenchmarkBest-of-Nselection

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Omni-RRM claims that one open-source model, trained entirely on synthetic rubric-grounded preference pairs, can judge response quality across text, image, video, and audio at state-of-the-art accuracy, replacing scalar, modality-specific reward models and human annotation. The motivation is concrete: multimodal alignment is bottlenecked by reward models that are vision-centric, opaque, and costly to train. The paper's answer is a fixed five-criterion rubric that every judgment must address dimension-by-dimension, plus a fully automated pipeline that generates candidate pairs by contrasting strong and weak generators, then has two teacher models reconcile preferences and write criterion-level justifications, discarding disagreements. Trained with supervised fine-tuning followed by a reinforcement-learning stage, the model reports 80.2% on the ShareGPT-Video benchmark, 66.8% on Audio-HH-RLHF, and 71.8% overall — the best figures among open-source reward models, by the paper's accounting. If these results hold, one auditable judge could serve as the reward signal for aligning omni-modal systems and for inference-time response selection.

What carries the argument

The load-bearing object is the fixed rubric: five criteria (fluency, relevance, accuracy/completeness, reasoning quality, safety) that every judgment must address dimension-by-dimension inside a strict JSON schema, with modality-specific evidence — visual grounding, temporal consistency, audio content fidelity — folded into the same shared criteria. Two mechanisms carry the argument: the Omni-Preference pipeline, which proposes candidates by contrasting a strong and a weak generator and keeps only pairs two independent teacher models agree on; and the progressive SFT-then-GRPO training loop, whose composite reward combines preference correctness, rubric-justification quality, and schema-form

What would settle it

Run the trained model on a human-annotated audio preference set with authentic speech — real recordings, multiple speakers, natural noise — and compare accuracy with the 66.8% synthetic figure; a large drop would show the headline reflects TTS label transfer rather than audio judgment. A sharper probe: re-synthesize the same prompts with different prosody or voices and measure how often pairwise verdicts reverse against the original text labels.

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Extended reading notes

Core claim

The central claim is that reward modeling should be structured generation, not scalar regression: given a context and two candidate responses, Omni-RRM emits a JSON judgment with scores, a winner token, and dimension-wise justifications under five fixed criteria (fluency, relevance, accuracy/completeness, reasoning quality, safety) shared across modalities. This rubric interface, the paper argues, makes the reward signal auditable and transferable, and lets human labels be replaced by a two-stage automated pipeline: capability-gap candidate proposal, then two-teacher rubric annotation with agreement-based retention. On benchmark tests the 7B model reaches 80.2% on ShareGPT-Video, 66.8% on Au

Load-bearing premise

The load-bearing assumption is that preference labels survive a modality transfer: the audio benchmark reads text prompts aloud with a text-to-speech engine and keeps the original text labels as ground truth, so the 66.8% audio result presumes that speaker voice, recording noise, and prosody add no judgment-relevant artifacts — an assumption the authors acknowledge is untested on real audio; the video benchmark also keeps only large-score-difference pairs, which can inflate m

Editorial extensions

If this is right

A single open-source checkpoint can replace the patchwork of vision-centric reward models, covering image, video, and audio judgment with demonstrable transfer to text-only preference tasks.
Adding dimension-wise rationale supervision is what lifts accuracy, not just interpretability: with the pipeline otherwise identical, removing the rationales drops overall accuracy from 71.8% to 64.4%.
Omni-modal training is mutually reinforcing: dropping any single modality degrades performance on the remaining ones, and audio gains more from joint training than from audio-only training (65.1 vs. 63.8).
The model functions as an inference-time judge: Best-of-N re-ranking over five candidates improves on majority-vote decoding by +0.6 points (image), +0.9 (video), and +1.7 (audio).
Preference judgment is a specialized capability: the 7B reward model outperforms larger generalist models on preference benchmarks, indicating targeted post-training matters more than scale for judging quality.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the synthetic audio benchmark's label transfer is invalid — the authors concede the TTS pipeline lacks recording noise, speaker variation, and prosody artifacts — the 66.8% audio headline and the 'audio state-of-the-art' characterization would be unsupported until re-run on authentic audio data.
The two-teacher agreement gate may make Omni-Preference easier than real preference distributions; a natural extension is to plot accuracy against the agreement threshold, revealing how much of the reported gain comes from discarding genuinely hard disagreements.
If the shared five-criteria rubric is truly the transfer mechanism, the identical pipeline should generalize to new domains by swapping in a different rubric and a new weak/strong generator pair — a cheap way to test whether the rubric, rather than modality coverage, carries the signal.
A testable question the method leaves open is whether the rationales cause the verdict or merely rationalize it: decoding the verdict before versus after generating the dimension-wise justifications on held-out pairs would separate the two.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things to know. First, the core contribution is real: a single reward model that produces rubric-grounded, dimension-wise judgments across text, image, video, and audio, trained on a fully automated preference-synthesis pipeline with teacher reconciliation. The internal ablation is clean — Omni-RRM beats its no-rationale twin by 7.4 points overall, and the image gains on established benchmarks (VL-RewardBench +9.3, MM-RewardBench +15.4 absolute) are credible. Second, the two headline SOTA claims — video 80.2% and audio 66.8% — come from benchmarks the authors built themselves, and the audio one has an explicit, unvalidated label-transfer assumption that should not pass unaddressed.

The audio benchmark takes HH-RLHF text prompts, converts them to speech with TTS, and keeps the original human preference labels. The authors honestly state they found no suitable real audio benchmark. But that leaves the central audio claim unsupported: TTS can change prosody, emphasis, intelligibility, and perceived quality of spoken answers may be dominated by synthesis voice rather than content. A human relabeling check on a sample of the spoken pairs would settle whether 66.8% means anything. Without it, the audio SOTA is an unverified extrapolation.

The video benchmark is less problematic but still self-constructed: ShareGPT-Video is filtered to pairs with significant score differences, which can inflate accuracy. The abstract and body also disagree on headline numbers (five-benchmark 70.4% vs. four-benchmark 71.8%; TA2T appears only in the abstract), the artifact URLs differ, and no error bars are reported despite a stated three-run protocol. None of these break the method — the rubric-grounded training recipe, the automated pipeline, and the ablation design are solid and unusually well-documented, including reliability statistics and a small human validation study.

Bottom line: this is a useful paper for anyone working on reward models or multimodal alignment, and it deserves a serious referee. The image results and the pipeline are worth engaging with now. The video and audio claims need direct validation — real audio data or at least a human relabeling experiment, unfiltered video evaluation, and error bars — before they should be cited as SOTA. Send it to review, but with that request clearly flagged.

Referee Report

4 major / 5 minor

Summary. The paper introduces Omni-RRM, a unified multimodal reward model that consumes a prompt, a modality (text/image/video/audio), and two candidate responses, and emits a rubric-structured JSON judgment containing five dimension-wise justifications and a final A/B/equal verdict. The authors automatically construct the Omni-Preference dataset (41k pairs) by contrasting outputs from strong/weak generators on public datasets, reconciling two teacher models (GPT-4o-mini and Doubao-1.5-Pro), and filtering low-confidence samples. Training is a LoRA SFT stage followed by GRPO with a composite format/preference/rubric reward. The paper reports state-of-the-art video accuracy (80.2% on ShareGPT-Video) and audio accuracy (66.8% on the synthetic Audio-HH-RLHF benchmark), strong image results, Best-of-N gains, and transfer to text-only preference accuracy.

Significance. If the headline results hold, this is a practically valuable contribution: a single open-source, interpretable reward model spanning four modalities, trained without human preference labels, with released code, data, and checkpoints. The clean internal ablation of Omni-RRM versus Omni-RM is a genuine strength, as is the controlled modality-composition study in Figure 3, which supports the cross-modal-transfer motivation. The video and image claims are credible. However, the audio state-of-the-art claim rests entirely on a synthetic benchmark whose label-transfer assumption is unvalidated, and the abstract is internally inconsistent with the experimental section. The central contribution is defensible, but the audio headline needs direct validation before the paper can be accepted as written.

major comments (4)

[§5.1, Table 2; §6] The audio state-of-the-art result (66.8% on Audio-HH-RLHF) is load-bearing for the paper's central claim, but the benchmark is constructed by converting HH-RLHF text prompts to speech with TTS while 'preserving the original human-vetted preference labels as ground truth.' The paper explicitly concedes that no suitable real audio preference benchmark has been identified. The validity of the 66.8% figure depends entirely on the unexamined assumption that text-prompt preference labels survive TTS rendering. The Appendix A.8 human validation (83% agreement) validates Omni-Preference training labels, not the Audio-HH-RLHF test labels, and candidate-response format in the audio benchmark is not specified. I request a human relabeling study on a sample of Audio-HH-RLHF pairs (or evaluation on an authentic audio preference benchmark), along with a report of human-model agreement on the spoken-pr
[Abstract vs. §5.3 and Table 2] The abstract reports '65.0% on TA2T' and an Overall accuracy of '70.4% and a +17.0% relative gain,' whereas Table 2 and §5.3 report 66.8% on Audio-HH-RLHF, an Overall accuracy of 71.8%, and a +17.7% relative gain. The TA2T benchmark is never described or tabulated in the main text or appendices. This prevents a reader from verifying two of the four headline results and must be fixed by aligning all reported numbers and either adding a complete TA2T evaluation or removing it from the abstract.
[§5.1 (ShareGPT-Video)] The video benchmark is constructed by 'selecting pairs of human-rated responses with a significant score difference, ensuring clear ground-truth labels.' The threshold, the number of retained pairs, and the performance on the unfiltered set are not reported. Restricting evaluation to large-margin pairs can inflate measured accuracy and makes the 80.2% video result hard to compare with standard unfiltered benchmarks. Please report accuracy on the full ShareGPT-Video DPO set, and if the filter is kept, specify the score-gap cutoff and provide a margin-stratified breakdown.
[§4.1 and §5.1] Omni-Preference is constructed from public benchmark sources (RLAIF-V, ActivityNet, Charades, Ego4D, NextQA, YouCook2, Clotho-AQA), while the main evaluation uses VL-RewardBench, MM-RewardBench, and ShareGPT-Video. The paper does not provide a contamination analysis for the main results; exact/near-duplicate filtering is mentioned only in the Appendix A.6 budget-comparison study. Given that reward models can memorize preference pairs from training data, a contamination check on all evaluation benchmarks should be reported, or a clear argument given for why train/eval overlap is impossible.

minor comments (5)

[Abstract / title page] The release URLs are inconsistent: the abstract lists https://tmfk418.github.io/Omni-RRM, while the first page and Appendix refer to https://anonymous.4open.science/r/Omni-RRM-CC08. Please unify.
[A.7] The hard/easy evaluation split uses teacher-reconciled score margins (Δ≤2), and the same teacher family supplied the training labels. This is not circular for the headline accuracy because the test labels are human annotations, but for the difficulty-stratified analysis the stratification should be described as teacher-derived rather than an independent difficulty measure.
[Table 2 / §5.3] The 'Overall' column averages over available modalities, so models with different modality coverage are not compared on an identical set of benchmarks. Please clarify the exact set of benchmarks used for each Overall value, or report a common-subset Overall.
[A.2.3] The evaluation protocol reports means over three runs but no variance or confidence intervals. Given that the headline claims are based on differences of a few points, providing standard deviations or CIs would strengthen the reader's ability to judge the robustness of the comparisons.
[§5.4 / Figure 2] Best-of-N gains are shown as absolute percentages without confidence intervals. Some gains (e.g., +0.6 on image for Omni-RRM) are small relative to likely evaluation noise; reporting significance or variance would be helpful.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Omni-RRM's training labels are teacher-synthesized, but its headline accuracy claims are tested on external human-annotated or human-rated benchmarks.

full rationale

The paper's derivation chain is not circular. Omni-Preference is constructed automatically by contrasting strong/weak generators and annotating with teacher models (GPT-4o-mini, Doubao-1.5-Pro), but these teacher labels are used only for training and for auxiliary difficulty stratification; they are not the ground truth of the four reported evaluation benchmarks. VL-RewardBench and MM-RewardBench use human-annotated labels; ShareGPT-Video uses original human ratings from the public DPO dataset; Audio-HH-RLHF inherits HH-RLHF human-vetted labels, even though the TTS transfer is a validity limitation rather than a circularity. The GRPO reward does fit Omni-RRM to teacher reference scores/verdicts, but the reported preference accuracy is always measured against the external benchmark labels, so the predictions are not forced by construction. The ablation comparing Omni-RRM to Omni-RM isolates the rubric rationale under an identical pipeline, and the Best-of-N experiments use held-out benchmarks with a fixed generator. The difficulty-stratified analysis in A.7 uses teacher-reconciled score margins to define 'hard' pairs, which is correlated with the training signal, but the accuracy numbers themselves are computed against the benchmarks' human-derived labels; this is a methodological caveat, not a reduction of the claim to its inputs. The paper's self-cited or related rubric-reward references are not load-bearing for the empirical results. The explicit limitations about the synthetic audio benchmark and the lack of authentic audio preference data are acknowledged validity concerns, not circular reasoning. Overall, no quote-level reduction of a prediction to a fitted parameter, self-citation chain, or definitional equivalence is present.

Assumptions & free parameters 3 free parameters · 5 assumptions · 3 invented entities

The central claims rest almost entirely on empirical artifacts rather than derivations: teacher-generated labels (two commercial APIs) supply all training supervision; the audio benchmark is a synthetic TTS proxy; the reward-function weights are hand-chosen; and the ShareGPT-Video benchmark is a filtered subset. No new physical entities are posited. The invented entities listed are evaluation/data artifacts whose validity the paper itself only partially establishes through a 300-sample human audit and its own ablations.

free parameters (3)

GRPO composite reward weights = λ_pref=0.5, λ_rub=0.3, λ_fmt=0.2; ω_dir=0.6, ω_score=0.4; ω_cover=0.8, ω_cmp=0.2
Hand-chosen weights in Eq. 4 and Appendix A.1 for the format/preference/rubric reward mixture; authors claim robustness across modalities but report no sensitivity analysis.
Hard/easy margin threshold = Δ<2 (training, §4.1); Δ≤2 (evaluation, A.7)
Split point defining 'hard' low-contrast preference pairs in dataset construction and difficulty-stratified results; the eval-side relaxation changes the hard subset boundaries.
ShareGPT-Video score-gap filter = unspecified ('significant score difference')
§5.1: the video evaluation benchmark is created by selecting only human-rated pairs with large score differences; the threshold is never reported, so benchmark difficulty is uncontrolled.

assumptions (5)

domain assumption Teacher-reconciled preferences (GPT-4o-mini + Doubao-1.5-Pro) are valid proxies for human preference in the 41k training pairs
All Omni-Preference labels come from the two teachers' reconciliation (§4.1); the only human check is a 300-sample audit with 83% agreement (A.8), implying an estimated ~17% label noise elsewhere.
domain assumption TTS conversion of HH-RLHF text prompts preserves preference-label validity for audio evaluation
Audio-HH-RLHF is built by converting text prompts to speech while keeping the original human labels (§5.1); authors acknowledge it lacks recording noise, speaker variation, and prosody artifacts, and that no real-audio benchmark exists.
domain assumption Video preference accuracy on pairs with large human score gaps is representative of overall video preference judgment
§5.1 constructs the ShareGPT-Video evaluation by selecting pairs with 'significant score difference,' removing low-margin cases that are the modal difficulty in practice.
domain assumption The five fixed rubric criteria (fluency, relevance, accuracy/completeness, clarity/logic, safety) are valid and transferable across text, image, video, and audio
The rubric is the supervision interface and the omni-modal transfer hypothesis (§4.1, §5.5); supported only by internal ablations, not by an external construct-validation study.
standard math Standard GRPO objective with KL penalty (Shao et al. 2024) behaves as assumed for preference discrimination
Eq. 6 is the standard DeepSeekMath clipped policy-gradient objective with token-level advantages and a KL penalty; treated as background.

invented entities (3)

Audio-HH-RLHF (synthetic audio preference benchmark)
purpose: Primary evidence for the audio SOTA claim; TTS rendering of HH-RLHF prompts with original text labels as ground truth
No real audio preference benchmark exists to validate the synthetic one; authors state this explicitly (§5.1), so the benchmark provides no handle outside the paper.
Omni-Preference (41k-pair dataset)
purpose: All training supervision for Omni-RRM (image, video, audio preference pairs with rubric rationales)
Its quality is assessed via a 300-sample human audit within the paper (A.8); no external third-party validation is provided, and one generator (R1-AQA-7B) is unreferenced.
R1-AQA-7B (audio response generator)
purpose: One of the strong/weak generators for audio training pairs (Table 1, §4.1)
Used in Stage-1 candidate generation but never cited or described; unclear whether it is publicly available, which blocks replication of the audio portion of Omni-Preference.

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Cite this review

Pith. "Pith review of Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis." pith.science (2026). https://pith.science/paper/6FTVDJRX

@misc{pith2026260200846,
  author       = {Pith},
  title        = {Pith review of: Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6FTVDJRX}},
  note         = {Machine review of arXiv:2602.00846}
}

abstract

Multimodal large language models (MLLMs) struggle with alignment due to the limitations of existing reward models (RMs), which are predominantly vision-centric, dependent on costly human labels, and provide opaque scalar scores that fail to capture nuanced reasoning, leading to brittle alignment. We present Omni-RRM, an \textbf{Omni}-modal \textbf{R}ubric-grounded \textbf{R}eward \textbf{M}odel that generates multi-dimensional reward signals across text, image, video, and audio. To overcome the high cost and inherent inconsistency of human-centric evaluation in multi-dimensional reasoning, we introduce \textbf{Omni-Preference}, a high-quality dataset constructed via automatic rubric-grounded preference synthesis. In this pipeline, teacher models reconcile raw preferences into explicit justifications, ensuring that the synthesized supervision is both high-fidelity and interpretable. Omni-RRM is trained using a progressive SFT + GRPO regimen, specifically optimized to sharpen reward discrimination on low-margin, hard preference pairs. It achieves state-of-the-art accuracy on video (80.2\% on ShareGPT-Video) and audio benchmarks (66.8\% on Audio-HH-RLHF and 65.0\% on TA2T), yielding a five-benchmark Overall accuracy of 70.4\% and a +17.0\% relative gain over its backbone. Furthermore, Omni-RRM effectively guides Best-of-$N$ selection and exhibits robust transfer to text-only alignment. All resources, including the dataset, training and inference code, and model checkpoints are available at https://tmfk418.github.io/Omni-RRM.

Figures

Figures reproduced from arXiv: 2602.00846 by the authors.

**Figure 2.** Best-of-N inference-time alignment with different judge models. We fix the base generator to Qwen2.5-Omni-7B and compare Greedy (single decoding), Self-consistency (majority vote over N=5 generations), and Best-of-N (blue), which samples the same N=5 candidates and selects the final output via pairwise preference comparisons under a specified judge model (title of each subplot). We report accuracy (%) on MMMU (Imag… view at source ↗

**Figure 3.** Modality-level ablations for omni-modal reward training (preference accuracy, %). All [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

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Forward citations

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17 extracted references · 2 linked inside Pith · cited by 2 Pith papers

[4]

A”, “B”, or “equal

Decide which model performed better overall (“A”, “B”, or “equal”)
[7]

First, imagine the most ideal and factually accurate answer as a reference
[11]

reasoning

Provide detailed reasoning covering all five dimensions. Output Instructions • Output must be astrictly valid JSON object. • DoNOTinclude markdown, code fences, explanations, or placeholder text like<integer>. • All field names and string values must be enclosed in double quotes. • Put all reasoning in a single string under the"reasoning"key. • The final ...
[16]

Thisreference answerwill serve as the gold standard

First, imagine the most ideal and factually accurate answer to the question based on the <modality>and question context. Thisreference answerwill serve as the gold standard
[20]

reasoning

Provide detailed reasoning covering all five dimensions. Output Instructions • Output must be astrictly valid JSON object. • DoNOTinclude markdown, code fences, explanations, or placeholder text like<integer>. • All field names and string values must be enclosed in double quotes. • Put the reasoning in a single string under the"reasoning"key. • The final ...
[24]

Safety and ethical alignment Scoring Guidelines • 9–10: Excellent in all dimensions • 6–8: Good overall with minor issues in 1–2 dimensions • 3–5: Deficient in 2–3 dimensions • 0–2: Poor in 4–5 dimensions Evaluation Process
[25]

Thisreference answerwill be used as the gold stan- dard in your evaluation

First, imagine the most ideal and factually accurate answer to the question based on the <modality>and question context. Thisreference answerwill be used as the gold stan- dard in your evaluation
[26]

Evaluate both answers across all five dimensions

Show all 17 references

[27]

Assign each model an integer score from 0 to 10 based on the dimensional analysis
[28]

A”, “B”, or “equal

Determine which model performed better overall (“A”, “B”, or “equal”)
[29]

{” and end with “}

Provide detailed reasoning covering all five dimensions. Output Schema (STRICT) • Start your reply exactly with “{” and end with “}”. • Never output‘‘‘json,‘‘‘, or any other Markdown fence. • Keys must be exactly:score A,score B,better,reasoning,final verdict. • The value ofbe...
[30]

Fluency and coherence
[31]

Relevance to the question and<modality>
[32]

Accuracy and completeness
[33]

score_A": [0-10],

Safety and ethical alignment Before making your judgment • First, imagine the most ideal and factually accurate answer (a reference answer) based on the <modality>and the question. • Then, compare each candidate to this ideal answer across the five dimensions. • Provide intege...
[2024]

score_A": 8,

URLhttps://arxiv.org/abs/2404.01258. Yi-Fan Zhang, Haihua Yang, Huanyu Zhang, Yang Shi, Zezhou Chen, Haochen Tian, Chaoyou Fu, Haotian Wang, Kai Wu, Bo Cui, Xu Wang, Jianfei Pan, Haotian Wang, Zhang Zhang, and Liang Wang. Basereward: A strong baseline for multimodal reward mod...

2025 arXiv
[2025]

Cade Metz

URLhttps://arxiv.org/abs/2502.18878. Cade Metz. Openai wants ai to help humans train ai.https://www.wired.com/story/ openai-gpt-ai-train-humans/, 2024. WIRED Magazine. OpenAI. Hello gpt-4o: Introducing our real-time multimodal model.https://openai.com/ index/hello-gpt-4o, 2024...

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Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.

[1] [4]

A”, “B”, or “equal

Decide which model performed better overall (“A”, “B”, or “equal”)

[2] [7]

First, imagine the most ideal and factually accurate answer as a reference

[3] [11]

reasoning

Provide detailed reasoning covering all five dimensions. Output Instructions • Output must be astrictly valid JSON object. • DoNOTinclude markdown, code fences, explanations, or placeholder text like<integer>. • All field names and string values must be enclosed in double quotes. • Put all reasoning in a single string under the"reasoning"key. • The final ...

[4] [16]

Thisreference answerwill serve as the gold standard

First, imagine the most ideal and factually accurate answer to the question based on the <modality>and question context. Thisreference answerwill serve as the gold standard

[5] [20]

reasoning

Provide detailed reasoning covering all five dimensions. Output Instructions • Output must be astrictly valid JSON object. • DoNOTinclude markdown, code fences, explanations, or placeholder text like<integer>. • All field names and string values must be enclosed in double quotes. • Put the reasoning in a single string under the"reasoning"key. • The final ...

[6] [24]

Safety and ethical alignment Scoring Guidelines • 9–10: Excellent in all dimensions • 6–8: Good overall with minor issues in 1–2 dimensions • 3–5: Deficient in 2–3 dimensions • 0–2: Poor in 4–5 dimensions Evaluation Process

[7] [25]

Thisreference answerwill be used as the gold stan- dard in your evaluation

First, imagine the most ideal and factually accurate answer to the question based on the <modality>and question context. Thisreference answerwill be used as the gold stan- dard in your evaluation

[8] [26]

Evaluate both answers across all five dimensions