Pith. sign in

REVIEW 4 major objections 5 minor 85 references

Pairwise Calibrated Rewards for Pluralistic Alignment

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Pairwise-calibrated ensembles of reward functions—trained on soft preference labels and needing no annotator identities—can faithfully represent the spread of human preferences, and only O(1/ε) rewards are needed for ε-approximate…

desk verdict A clean formal criterion for pluralistic rewards with real existence results, but the theoretical guarantee doesn't transfer to the learned heuristic and the empirical evaluation is underpowered. read the letter →

arxiv 2506.06298 v1 pith:563VPESC submitted 2025-05-17 cs.LG cs.AI

Daniel Halpern , Evi Micha , Ariel D. Procaccia , Itai Shapira This is my paper · ORCID
classification cs.LGcs.AI
keywords pluralisticalignmentpairwisecalibrationrewardensemblessoftlabelspreferencediversityRLHFforwardstagewiseadditivemodelingoutlierpruning
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

Standard alignment trains one reward function from pooled human preferences, so disagreements collapse into a majority vote and minority perspectives are lost. This paper replaces the single reward with a small ensemble of reward functions whose collective pairwise choices are required to match the observed fraction of annotators preferring each response—a property it calls pairwise calibration. The paper proves that an O(1/ε)-sized ensemble can achieve ε calibration error, that the needed rewards need not include extreme outliers, and that finite data suffice to learn such ensembles. It then introduces a stagewise heuristic that trains each new reward on the residual calibration error of the current ensemble, and shows on four preference datasets that two to four such rewards already beat the best single deterministic reward on held-out calibration error. If the approach holds, pluralistic alignment can preserve minority viewpoints and response diversity without demographic grouping or annotator identities.

What carries the argument

The load-bearing object is the pairwise calibration error L(r) = E[(p̂_r(x,y1,y2) − p*(x,y1,y2))²], where p̂_r is the weighted fraction of ensemble rewards preferring y1 over y2 and p* is the annotator fraction. The existence argument samples O(1/ε) annotator reward functions uniformly, the hardness argument reduces Minimum Feedback Arc Set to membership in the linear ordering polytope, the outlier argument uses a Markov step and triangle inequalities on the disagreement score Φ(θ), and the generalization argument bounds the pseudo-dimension of the k-ensemble loss class. The practical machinery is forward stagewise additive modeling: at iteration j, train r_θj to minimize the squared residual εj = p − p̂_{r_{j−1}} using a sigmoid proxy for the vote indicator, then re-optimize the weights α1,...,αj. Lemma 1—which decomposes empirical loss as population loss plus the binomial variance of n annotator votes—is what makes soft-label training a sound estimation strategy despite the bias.

What would settle it

Synthesize a population with annotators whose pairwise preferences are cyclic (A≻B, B≻C, C≻A) so that no scalar reward function can induce them; if FSAM with growing k still reaches ε-pairwise calibration on held-out triples, then the reward-inducibility assumption is not necessary, while if calibration error stays at the single-reward floor, the assumption is load-bearing.

Watch

Extended reading notes

Core claim

The central claim is that the correct object for pluralistic alignment is not a single reward but a weighted mixture of reward functions that is pairwise calibrated: for every pair of responses, the mixture's probability of preferring one response equals the fraction of annotators who prefer it. Pairwise calibration is defined through the mean squared error between the ensemble's vote share and the observed soft-label frequency p*(x, y1, y2), so annotator disagreement is treated as signal rather than noise. The paper proves existence via the probabilistic method—sampling about 1/ε annotator reward functions uniformly yields expected squared error at most 1/(4k)—and shows that finding a perfectly calibrated ensemble is NP-hard, which motivates approximation. It also proves that the total weight of (β, γ)-outlier rewards is at most 1/(β−1), so nearly calibrated outlier-free ensembles exist, and that empirical loss on soft labels estimates the population loss up to an irreducible binomial noise term with a finite VC-dimension bound. The practical FSAM procedure fits each new reward model to the current residual and re-optimizes mixture weights; on four datasets, ensembles of size two to four achieve lower held-out mean-squared calibration error than the optimal single deterministic majority-vote reward, and the learned rewards are measurably diverse.

Load-bearing premise

The guarantees assume every annotator's preferences are exactly captured by some reward function in the chosen class; if real preferences fall outside that class, the theorems about existence and outlier pruning no longer apply to learned ensembles.

Editorial extensions

If this is right

  • A small ensemble of size O(1/ε) is enough to approximate any pairwise preference distribution, so faithful pluralism does not require one reward per annotator.
  • Nearly calibrated ensembles can be pruned of extreme outlier reward functions: their total weight is bounded by 1/(β−1), and removing them costs at most (√ε + 1/(β−1))² in squared calibration error.
  • Learning from finite soft-label data is statistically sound: minimizing empirical MSE estimates the population calibration loss up to an irreducible binomial noise term, with sample complexity O(k d log(k d)).
  • The FSAM heuristic produces ensembles whose held-out calibration error drops below the single-reward majority floor within two to four iterations on the tested datasets, and the component rewards are not duplicates of each other.
  • Deployment can follow balanced, steerable, or distributional modes, so a pairwise-calibrated ensemble supports both individualized outputs and population-level response diversity.

Reading between the lines

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

  • A natural extension beyond the paper is to test the theory on synthetic mixtures of known reward functions inside the model class, checking whether FSAM recovers pairwise calibration at the predicted O(1/ε) rate and whether outlier pruning matches the theorem's weight bound.
  • Because calibration is defined on pairs only, higher-order judgments over sets of responses are information-theoretically unrecoverable from pairwise data alone; soliciting best-of-ℓ rankings could constrain the ensemble and strengthen pluralistic fidelity.
  • The empirical gains are shown with small base models and modest ensemble size; scaling to larger base models or larger k would reveal whether the calibration gap persists, saturates, or inverts.
  • In deployment, sampling a policy from the ensemble in proportion to its weight may reduce preference collapse over many queries; this effect could be measured directly against a single-reward baseline.
  • If annotator rewards lie outside the chosen reward class, the theoretical guarantees do not directly apply; a falsifiable probe is to corrupt a known reward mixture with non-transitive or context-dependent preferences and measure how calibration degrades with k.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes a distribution over reward functions—rather than a single scalar reward—as a model of pluralistic human preferences, and introduces pairwise calibration as the criterion: for every pair of responses, the ensemble's fraction of rewards preferring one response should match the annotator preference fraction. Theoretically, it proves that finding a perfectly calibrated ensemble is NP-hard (Theorem 1), that an O(1/ε)-sized ensemble suffices for ε-pairwise calibration (Theorem 2), that outliers can be pruned without much calibration loss (Theorem 3), and that finite-sample uniform convergence holds under a finite VC-dimension assumption (Theorem 4). Empirically, it proposes a forward stagewise additive modeling (FSAM) heuristic that greedily fits reward models to residual calibration errors, and reports lower held-out MSE than a single deterministic reward's lower bound on four preference datasets. The paper includes detailed deferred proofs, a discussion of non-identifiability, and explicit limitations concerning higher-order preferences and sensitive topics.

Significance. If the claims hold, the paper makes a useful conceptual contribution: a formally defined, theoretically motivated notion of pluralistic reward distribution that does not require annotator identities or predefined groups, together with an implementation heuristic. The existence and outlier-pruning theorems are nontrivial and are proved with explicit constructions, and the paper is commendably transparent about non-identifiability and about the fact that pairwise data cannot recover higher-order preference structure. The empirical section also addresses a real gap by using soft labels rather than majority-vote collapse. However, the significance is tempered by the gap between the existence theorems, which are proved for exact annotator reward functions, and the learned FSAM ensembles, which operate in a restricted function class on finite noisy labels; the paper does not establish that the theoretical guarantees transfer to the learned models. The empirical claim is also currently demonstrated only against a theoretical lower bound rather than against actual alternative training procedures.

major comments (4)
  1. [§3.1, Theorem 2 and §4] Theorem 2 constructs an ensemble by sampling uniformly from the annotators' true reward functions r_{θ_i}, relying on the Section 2 assumption that each annotator is reward-inducible within Θ. The FSAM procedure in Section 4 instead trains reward models on aggregated, noisy soft labels p in a restricted linear-head class over frozen Llama-3-8B embeddings. Nothing in Theorem 2 or Theorem 4 shows that this learned class contains a low-calibration-error ensemble, so the paper's central theoretical guarantee does not transfer to the empirically evaluated method. The assumption is stated, but its failure mode is neither analyzed nor tested; this is a load-bearing gap between the two contributions.
  2. [§3.3, Theorem 4] Theorem 4 is conditional on F having finite VC dimension d, but the manuscript does not verify this for the actual models, and the running example of linear heads on Llama-3-8B embeddings gives d on the order of the embedding dimension (roughly 4096). For the smaller datasets (e.g., Reddit TL;DR with N=3,217), d' = 20(d+1)k log(2(d+1)k) exceeds N, so the bound's log(eN/d') term is negative and the guarantee is vacuous. The theorem is internally correct as a conditional statement, but the claim that pairwise calibration can be learned with a limited number of comparisons is not established for the models and dataset sizes used in Section 5.
  3. [§5, Figure 2] The main empirical claim is that ensembles achieve lower held-out calibration MSE than an optimal single deterministic reward, but the comparison is only against the theoretical lower bound Σ_i min{p_i², (1-p_i)²}, not against an actual single reward model trained with the same soft-label MSE objective or against existing pluralistic baselines. Since FSAM directly optimizes the evaluation metric on training data, reporting a gain over a mathematical floor does not show that the procedure outperforms a standard soft-label-trained single model on held-out data. The experiments also report no error bars, confidence intervals, or multiple seeds, so the stability of the gains across dataset splits is unknown.
  4. [§2 and §5] The empirical evaluation defines the target p as the observed annotator fraction, and the held-out MSE is computed against the same noisy p, not against the true population fraction p*. As Lemma 1 correctly notes, the empirical loss is biased by an irreducible label noise term C. Consequently, the reported numbers are not estimates of the true calibration error L(r), and the gap to p* is not quantified. This is not a fatal flaw, but it should be explicitly acknowledged when interpreting Figure 2, and ideally the paper should bound or estimate the magnitude of C for the datasets used.
minor comments (5)
  1. [§3.1, proof of Theorem 2] In the proof, the text states that k ≥ 1/(4ε) implies ε ≤ 1/(4k), but the implication is reversed; the correct conclusion is ε ≥ 1/(4k), which is the inequality actually needed for the argument.
  2. [§3.3, Theorem 4 statement] The comparison function class F is defined with a non-strict inequality 1[r_θ(x,y₁) ≥ r_θ(x,y₂)], whereas Definition 1 and the rest of the paper use strict inequalities 1[r_θ(x,y₁) > r_θ(x,y₂)]. The tie case should be clarified for consistency.
  3. [§5, Figure 2] The caption refers to a shaded band marking the single-reward floor, but in the displayed figure the floor appears as a solid horizontal line; the visual encoding should be made consistent.
  4. [§F.2, Table 2] The dataset description for PersonalLLM notes that annotations come from multiple reward models rather than humans; this is appropriate for a stress test, but the paper should state more prominently in Section 5 that one of the four empirical results is based on synthetic model-based annotators, not human annotators.
  5. [§5, Figure 3] The Kendall–τ correlation is computed over only 50 prompts and 100 continuations per prompt; this is a small sample for the diversity claim, and the caption should report standard errors or another measure of variability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the theorem chain is self-contained and the empirical claim is standard held-out validation, not a reduction to the paper's own inputs.

full rationale

The derivation chain is self-contained. Theorems 1-4 are proved from the explicitly stated generative model (reward-inducible annotators, p* in Eq. 1, ensemble predictions in Eq. 2) and do not rely on fitted constants: Theorem 2 is a probabilistic-method existence proof by sampling the true annotator reward functions, Theorem 3 is a Markov/triangle-inequality pruning argument, and Theorem 4 is a standard VC/pseudo-dimension uniform-convergence bound. The FSAM procedure is explicitly labeled a heuristic, and while its objective (Eq. 2/residual MSE) has the same functional form as the reported Brier/MSE evaluation metric, the evaluation is on held-out prompts and the paper explicitly acknowledges the bias between empirical soft-label loss and the true calibration loss in Lemma 1. The self-citations ([32], [37], [39], [41]) supply background or standard non-identifiability facts and are not load-bearing for any theorem. The gap between the existence guarantee and the learned FSAM ensembles is a transfer/assumption limitation rather than a circular step, and the reward-inducibility assumption is stated in Section 2.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The theoretical results assume access to true annotator reward functions and a reward class that can represent them. The practical method replaces this with fine-tuned neural networks, so the guarantees are conditional on representability. The only explicit fitted ensemble-level quantities are the mixture weights α; reward parameters are learned by fine-tuning.

free parameters (1)
  • ensemble mixture weights α_j = not reported (k up to 8)
    Reoptimized after each FSAM iteration to minimize training MSE (Section 4). These weights define the final ensemble and are fitted to the preference data.
assumptions (5)
  • domain assumption Annotator preferences are reward-inducible: for each annotator i there exists rθ in Θ such that y1≻i y2 iff rθ(x,y1)>rθ(x,y2)
    Stated at start of Section 2. Theorems 2 and 3 rely on having access to annotators' true reward functions; if the reward class cannot represent actual preferences, the existence guarantees do not transfer to practice.
  • domain assumption Reward functions never tie
    Footnote 1 in Section 2: ties form a measure-zero set under continuous parameters; used to define indicator preferences.
  • domain assumption Underlying distributions DX over contexts and π0 over base responses exist, and annotators are drawn uniformly from population N
    Needed to define p⋆ and the MSE calibration objective. Assumes a stable population across contexts.
  • domain assumption The comparison function class F has finite VC dimension d
    Theorem 4 generalization bound assumes finite VC dimension; the actual Llama-3-8B reward models fine-tuned in Section 5 are not shown to satisfy this.
  • standard math Grötschel, Lovász, and Schrijver's optimization-with-membership-oracle theorem and McGarvey's theorem
    Used in the proof of Theorem 1 to reduce MFAS to tournament graph membership. Standard results in convex geometry and social choice.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Pairwise Calibrated Rewards for Pluralistic Alignment." pith.science (2026). https://pith.science/paper/563VPESC

@misc{pith2026250606298,
  author       = {Pith},
  title        = {Pith review of: Pairwise Calibrated Rewards for Pluralistic Alignment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/563VPESC}},
  note         = {Machine review of arXiv:2506.06298}
}
read the original abstract

Current alignment pipelines presume a single, universal notion of desirable behavior. However, human preferences often diverge across users, contexts, and cultures. As a result, disagreement collapses into the majority signal and minority perspectives are discounted. To address this, we propose reflecting diverse human preferences through a distribution over multiple reward functions, each inducing a distinct aligned policy. The distribution is learned directly from pairwise preference without annotator identifiers or predefined groups. Instead, annotator disagreements are treated as informative soft labels. Our central criterion is pairwise calibration: for every pair of candidate responses, the proportion of reward functions preferring one response matches the fraction of annotators with that preference. We prove that even a small outlier-free ensemble can accurately represent diverse preference distributions. Empirically, we introduce and validate a practical training heuristic to learn such ensembles, and demonstrate its effectiveness through improved calibration, implying a more faithful representation of pluralistic values.

Figures

Figures reproduced from arXiv: 2506.06298 by the authors.

Figure 1
Figure 1. We explicitly model human preferences as a distribution over reward functions without [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. MSE calibration error on held-out prompts as a function of ensemble size [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Pairwise Kendall–τ correlation scores between reward models in each ensemble, obtained by ranking 100 high-temperature continuations for 50 prompts. A score of 0 corresponds to random rankings, while a score of 1 corresponds to identical rankings; lower scores thus indicate greater diversity. The REDDIT TL;DR dataset—the smallest and focused on summarization—records the highest scores. Thus, combining a handful of w… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Histogram of the pairwise soft-label values, where [PITH_FULL_IMAGE:figures/full_fig_p025_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

85 extracted references · 40 canonical work pages

  1. [1]

    Training language models to follow instructions with human feedback

    Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. Training language models to follow instructions with human feedback....

    2022

  2. [2]

    Rank analysis of incomplete block designs: I

    Ralph Allan Bradley and Milton E Terry. Rank analysis of incomplete block designs: I. the method of paired comparisons.Biometrika, 39(3/4):324–345, 1952

    1952

  3. [3]

    Constitutional AI: Harmlessness from AI feedback

    Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional AI: Harmlessness from AI feedback. arXiv:2212.08073, 2022

    arXiv 2022

  4. [4]

    Ethical and social risks of harm from language models

    Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, et al. Ethical and social risks of harm from language models. arXiv:2112.04359, 2021

    arXiv 2021

  5. [5]

    Cultural palette: Pluralising culture alignment via multi-agent palette

    Jiahao Yuan, Zixiang Di, Shangzixin Zhao, and Usman Naseem. Cultural palette: Pluralising culture alignment via multi-agent palette. arXiv:2412.11167, 2024

    arXiv 2024

  6. [6]

    Cultural incongruencies in artificial intelligence

    Vinodkumar Prabhakaran, Rida Qadri, and Ben Hutchinson. Cultural incongruencies in artificial intelligence. arXiv:2211.13069, 2022

    arXiv 2022

  7. [7]

    Foundational challenges in assuring alignment and safety of large language models

    Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, et al. Foundational challenges in assuring alignment and safety of large language models. arXiv:2404.09932, 2024

    arXiv 2024

  8. [8]

    The PRISM alignment dataset: What participatory, representative and individualised human feedback reveals about the subjective and multicultural alignment of large language models

    Hannah Rose Kirk, Alexander Whitefield, Paul Rottger, Andrew M Bean, Katerina Margatina, Rafael Mosquera-Gomez, Juan Ciro, Max Bartolo, Adina Williams, He He, et al. The PRISM alignment dataset: What participatory, representative and individualised human feedback reveals about the subjective and multicultural alignment of large language models. InProceedi...

    2024

Show all 85 references
  1. [9]

    MaxMin-RLHF: Towards equitable alignment of large language models with diverse human preferences

    Souradip Chakraborty, Jiahao Qiu, Hui Yuan, Alec Koppel, Furong Huang, Dinesh Manocha, Amrit Singh Bedi, and Mengdi Wang. MaxMin-RLHF: Towards equitable alignment of large language models with diverse human preferences. arXiv:2402.08925, 2024

    2024 arXiv

  2. [10]

    Open problems and fundamental limitations of reinforcement learning from human feedback

    Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, J´er´emy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, et al. Open problems and fundamental limitations of reinforcement learning from human feedback. arXiv:2307.15217, 2023

    2023 arXiv

  3. [11]

    Whose opinions do language models reflect? InProceedings of the 40th In- ternational Conference on Machine Learning (ICML), pages 29971–30004, 2023

    Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. Whose opinions do language models reflect? InProceedings of the 40th In- ternational Conference on Machine Learning (ICML), pages 29971–30004, 2023

    2023

  4. [12]

    Discovering language model behaviors with model-written evaluations

    Ethan Perez, Sam Ringer, Kamil˙e Lukoˇsi¯ut˙e, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, et al. Discovering language model behaviors with model-written evaluations. arXiv:2212.09251, 2022

    2022 arXiv

  5. [13]

    Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

    Hannah Rose Kirk, Bertie Vidgen, Paul R ¨ottger, and Scott A Hale. Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback. arXiv:2303.05453, 2023

    2023 arXiv

  6. [14]

    Diverse preference learning for capabilities and alignment

    Stewart Slocum, Asher Parker-Sartori, and Dylan Hadfield-Menell. Diverse preference learning for capabilities and alignment. InProceedings of the 13th International Conference on Learning Representations (ICLR), 2025. Forthcoming

    2025

  7. [15]

    Proximal policy optimization algorithms

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv:1707.06347, 2017. 11

    2017 arXiv

  8. [16]

    Jiancong Xiao, Ziniu Li, Xingyu Xie, Emily Getzen, Cong Fang, Qi Long, and Weijie J. Su. On the algorithmic bias of aligning Large language models with RLHF: Preference collapse and matching regularization. arXiv:2405.16455, 2024

    2024 arXiv

  9. [17]

    Evaluating the diversity and quality of LLM generated content

    Alexander Shypula, Shuo Li, Botong Zhang, Vishakh Padmakumar, Kayo Yin, and Osbert Bastani. Evaluating the diversity and quality of LLM generated content. arXiv:2504.12522, 2025

    2025

  10. [18]

    From distributional to Overton pluralism: Investi- gating large language model alignment

    Thom Lake, Eunsol Choi, and Greg Durrett. From distributional to Overton pluralism: Investi- gating large language model alignment. arXiv:2406.17692, 2024

    2024 arXiv

  11. [19]

    Understanding the effects of RLHF on LLM generalisation and diversity

    Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, and Roberta Raileanu. Understanding the effects of RLHF on LLM generalisation and diversity. arXiv:2310.06452, 2023

    2023 arXiv

  12. [20]

    A distributional approach to con- trolled text generation

    Muhammad Khalifa, Hady Elsahar, and Marc Dymetman. A distributional approach to con- trolled text generation. InProceedings of the 9th International Conference on Learning Representations (ICLR), 2021

    2021

  13. [21]

    Red teaming language models with language models

    Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. Red teaming language models with language models. arXiv:2202.03286, 2022

    2022 arXiv

  14. [22]

    Modular pluralism: Pluralistic alignment via multi-LLM collaboration

    Shangbin Feng, Taylor Sorensen, Yuhan Liu, Jillian Fisher, Chan Young Park, Yejin Choi, and Yulia Tsvetkov. Modular pluralism: Pluralistic alignment via multi-LLM collaboration. arXiv:2406.15951, 2024

    2024 arXiv

  15. [23]

    A roadmap to pluralistic alignment

    Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christo- pher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, et al. A roadmap to pluralistic alignment. arXiv:2402.05070, 2024

    2024 arXiv

  16. [24]

    RLHF from heterogeneous feedback via personalization and preference aggregation

    Chanwoo Park, Mingyang Liu, Dingwen Kong, Kaiqing Zhang, and Asuman Ozdaglar. RLHF from heterogeneous feedback via personalization and preference aggregation. arXiv:2405.00254, 2024

    2024 arXiv

  17. [25]

    Pal: Pluralistic align- ment framework for learning from heterogeneous preferences

    Daiwei Chen, Yi Chen, Aniket Rege, and Ramya Korlakai Vinayak. Pal: Pluralistic align- ment framework for learning from heterogeneous preferences. InProceedings of the 13th International Conference on Learning Representations (ICLR), 2025

    2025

  18. [26]

    Value profiles for encoding human variation

    Taylor Sorensen, Pushkar Mishra, Roma Patel, Michael Henry Tessler, Michiel Bakker, Georgina Evans, Iason Gabriel, Noah Goodman, and Verena Rieser. Value profiles for encoding human variation. arXiv:2503.15484, 2025

    2025

  19. [27]

    Aligning language models to user opinions

    Eunjeong Hwang, Bodhisattwa Prasad Majumder, and Niket Tandon. Aligning language models to user opinions. InProceedings of the 28th Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023

    2023

  20. [28]

    Persona: A reproducible testbed for pluralistic alignment

    Louis Castricato, Nathan Lile, Rafael Rafailov, Jan-Philipp Fr¨anken, and Chelsea Finn. Persona: A reproducible testbed for pluralistic alignment. arXiv:2407.17387, 2024

    2024 arXiv

  21. [29]

    Position: Social choice should guide AI alignment in dealing with diverse human feedback

    Vincent Conitzer, Rachel Freedman, Jobst Heitzig, Wesley H Holliday, Bob M Jacobs, Nathan Lambert, Milan Moss´e, Eric Pacuit, Stuart Russell, Hailey Schoelkopf, et al. Position: Social choice should guide AI alignment in dealing with diverse human feedback. InProceedings of th...

    2024

  22. [30]

    Ai alignment and social choice: Fundamental limitations and policy implica- tions

    Abhilash Mishra. Ai alignment and social choice: Fundamental limitations and policy implica- tions. arXiv:2310.16048, 2023

    2023 arXiv

  23. [31]

    Rewarded soups: towards Pareto-optimal alignment by inter- polating weights fine-tuned on diverse rewards

    Alexandre Rame, Guillaume Couairon, Corentin Dancette, Jean-Baptiste Gaya, Mustafa Shukor, Laure Soulier, and Matthieu Cord. Rewarded soups: towards Pareto-optimal alignment by inter- polating weights fine-tuned on diverse rewards. InProceedings of the 36th Annual Conference o...

    2024

  24. [32]

    Axioms for AI alignment from human feedback

    Luise Ge, Daniel Halpern, Evi Micha, Ariel D Procaccia, Itai Shapira, Yevgeniy V orobeychik, and Junlin Wu. Axioms for AI alignment from human feedback. InProceedings of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS), pages 80439–80465, 2024

    2024

  25. [33]

    Mallowspo: Fine- tune your LLM with preference dispersions.arXiv preprint arXiv:2405.14953, 2024

    Haoxian Chen, Hanyang Zhao, Henry Lam, David Yao, and Wenpin Tang. Mallowspo: Fine- tune your LLM with preference dispersions.arXiv preprint arXiv:2405.14953, 2024

    2024 arXiv

  26. [34]

    Adaptive preference scaling for reinforcement learning with human feedback.Advances in Neural Information Processing Systems, 37:107249–107269, 2024

    Ilgee Hong, Zichong Li, Alexander Bukharin, Yixiao Li, Haoming Jiang, Tianbao Yang, and Tuo Zhao. Adaptive preference scaling for reinforcement learning with human feedback.Advances in Neural Information Processing Systems, 37:107249–107269, 2024

    2024

  27. [35]

    Aligning language models with human preferences via a Bayesian approach.Advances in Neural Information Processing Systems, 36:49113–49132, 2023

    Jiashuo Wang, Haozhao Wang, Shichao Sun, and Wenjie Li. Aligning language models with human preferences via a Bayesian approach.Advances in Neural Information Processing Systems, 36:49113–49132, 2023

    2023

  28. [36]

    Personal- izing reinforcement learning from human feedback with variational preference learning.arXiv preprint arXiv:2408.10075, 2024

    Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, and Natasha Jaques. Personal- izing reinforcement learning from human feedback with variational preference learning.arXiv preprint arXiv:2408.10075, 2024

    2024 arXiv

  29. [37]

    Direct alignment with heterogeneous preferences

    Ali Shirali, Arash Nasr-Esfahany, Abdullah Alomar, Parsa Mirtaheri, Rediet Abebe, and Ariel Procaccia. Direct alignment with heterogeneous preferences. arXiv:2502.16320, 2025

    2025 arXiv

  30. [38]

    Distributional preference learning: Understanding and accounting for hidden context in RLHF

    Anand Siththaranjan, Cassidy Laidlaw, and Dylan Hadfield-Menell. Distributional preference learning: Understanding and accounting for hidden context in RLHF. InProceedings of the 12th International Conference on Learning Representations (ICLR), 2024

    2024

  31. [39]

    Clone-robust AI alignment

    Ariel D Procaccia, Benjamin Schiffer, and Shirley Zhang. Clone-robust AI alignment. In Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025. Forth- coming

    2025

  32. [40]

    Fine-tuning language models to find agreement among humans with diverse preferences

    Michiel Bakker, Martin Chadwick, Hannah Sheahan, Michael Tessler, Lucy Campbell- Gillingham, Jan Balaguer, Nat McAleese, Amelia Glaese, John Aslanides, Matt Botvinick, et al. Fine-tuning language models to find agreement among humans with diverse preferences. InProceedings of ...

    2022

  33. [41]

    Generative social choice

    Sara Fish, Paul G¨olz, David C Parkes, Ariel D Procaccia, Gili Rusak, Itai Shapira, and Manuel W¨uthrich. Generative social choice. InProceedings of the 25th ACM Conference on Economics and Computation (EC), page 985, 2024

    2024

  34. [42]

    Cambridge University Press, 2016

    Felix Brandt, Vincent Conitzer, Ulle Endriss, J ´erˆome Lang, and Ariel D Procaccia.Handbook of Computational Social Choice. Cambridge University Press, 2016

    2016

  35. [43]

    The MIT Press, 2012

    Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.Foundations of Machine Learning. The MIT Press, 2012

    2012

  36. [44]

    Friedman.The Elements of Statistical Learning: Data Mining, Inference, and Prediction

    Trevor Hastie, Robert Tibshirani, and Jerome H. Friedman.The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2nd edition, 2009

    2009

  37. [45]

    The past, present and better future of feedback learning in large language models for subjective human preferences and values

    Hannah Rose Kirk, Andrew M Bean, Bertie Vidgen, Paul R¨ottger, and Scott A Hale. The past, present and better future of feedback learning in large language models for subjective human preferences and values. arXiv:2310.07629, 2023

    arXiv

  38. [46]

    Zhang, Xinyi Chen, Qiuyi Zhang, Rajesh Ranganath, and Kyunghyun Cho

    Angelica Chen, Sadhika Malladi, Lily H. Zhang, Xinyi Chen, Qiuyi Zhang, Rajesh Ranganath, and Kyunghyun Cho. Preference learning algorithms do not learn preference rankings. InPro- ceedings of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS), pages...

    2024

  39. [47]

    Margin matching preference optimization: Enhanced model alignment with granular feedback

    Kyuyoung Kim, Ah Jeong Seo, Hao Liu, Jinwoo Shin, and Kimin Lee. Margin matching preference optimization: Enhanced model alignment with granular feedback. arXiv:2410.03145, 2024. 13

    2024 arXiv

  40. [48]

    The history and risks of reinforcement learning and human feedback

    Nathan Lambert, Thomas Krendl Gilbert, and Tom Zick. The history and risks of reinforcement learning and human feedback. arXiv:2310.13595, 2023

    2023 arXiv

  41. [49]

    Online, 2024

    Nathan Lambert.Reinforcement Learning from Human Feedback. Online, 2024. URL https://rlhfbook.com

    2024

  42. [50]

    On releasing annotator- level labels and information in datasets

    Vinodkumar Prabhakaran, Aida Mostafazadeh Davani, and Mark Diaz. On releasing annotator- level labels and information in datasets. arXiv:2110.05699, 2021

    2021 arXiv

  43. [51]

    Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned

    Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al. Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv:2209.07858, 2022

    2022 arXiv

  44. [52]

    Learning to summarize with human feedback

    Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea V oss, Alec Radford, Dario Amodei, and Paul F Christiano. Learning to summarize with human feedback. InProceedings of the 33th Annual Conference on Neural Information Processing Systems (NeurIPS), page...

    2020

  45. [53]

    Llama 2: Open foundation and fine-tuned chat models

    Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv:2307.09288, 2023

    2023 arXiv

  46. [54]

    Training a helpful and harmless assistant with reinforcement learning from human feedback

    Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv:2204.05862, 2022

    2022 arXiv

  47. [55]

    When does label smoothing help? InProceedings of the 32th Annual Conference on Neural Information Processing Systems (NeurIPS), 2019

    Rafael M¨uller, Simon Kornblith, and Geoffrey E Hinton. When does label smoothing help? InProceedings of the 32th Annual Conference on Neural Information Processing Systems (NeurIPS), 2019

    2019

  48. [56]

    Secrets of RLHF in large language models part ii: Reward modeling.arXiv preprint arXiv:2401.06080, 2024

    Binghai Wang, Rui Zheng, Lu Chen, Yan Liu, Shihan Dou, Caishuang Huang, Wei Shen, Senjie Jin, Enyu Zhou, Chenyu Shi, et al. Secrets of RLHF in large language models part ii: Reward modeling.arXiv preprint arXiv:2401.06080, 2024

    2024 arXiv

  49. [57]

    Vpo: Leveraging the number of votes in preference optimization

    Jae Hyeon Cho, Minkyung Park, and Byung-Jun Lee. Vpo: Leveraging the number of votes in preference optimization. arXiv:2410.22891, 2024

    2024 arXiv

  50. [58]

    Geometric-averaged preference optimization for soft preference labels

    Hiroki Furuta, Kuang-Huei Lee, Shixiang Shane Gu, Yutaka Matsuo, Aleksandra Faust, Heiga Zen, and Izzeddin Gur. Geometric-averaged preference optimization for soft preference labels. InProceedings of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS)...

    2024

  51. [59]

    Hybrid preferences: Learning to route instances for human vs

    Lester James V Miranda, Yizhong Wang, Yanai Elazar, Sachin Kumar, Valentina Pyatkin, Faeze Brahman, Noah A Smith, Hannaneh Hajishirzi, and Pradeep Dasigi. Hybrid preferences: Learning to route instances for human vs. AI feedback. arXiv:2410.19133, 2024

    arXiv

  52. [60]

    PersonalLLM: Tailoring LLMs to individual preferences

    Thomas P Zollo, Andrew Wei Tung Siah, Naimeng Ye, Ang Li, and Hongseok Namkoong. PersonalLLM: Tailoring LLMs to individual preferences. arXiv:2409.20296, 2024

    2024 arXiv

  53. [61]

    Helpsteer2: Open-source dataset for training top-performing reward models

    Zhilin Wang, Yi Dong, Olivier Delalleau, Jiaqi Zeng, Gerald Shen, Daniel Egert, Jimmy J Zhang, Makesh Narsimhan Sreedhar, and Oleksii Kuchaiev. Helpsteer2: Open-source dataset for training top-performing reward models. arXiv:2406.08673, 2024

    2024 arXiv

  54. [62]

    Ziegler, Ryan Lowe, Chelsea V oss, Alec Radford, Dario Amodei, and Paul Christiano

    Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea V oss, Alec Radford, Dario Amodei, and Paul Christiano. Learning to summarize from human feedback. In NeurIPS, 2020

    2020

  55. [63]

    Llama 3 model card

    AI@Meta. Llama 3 model card. https://github.com/meta-llama/llama3/blob/main/ MODEL_CARD.md, 2024. Accessed 11 May 2025

    2024

  56. [64]

    WebGPT: Browser-assisted question-answering with human feedback

    Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christo- pher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. WebGPT: Browser-assisted question-answering with human feedback. arXiv:2112.09332, 2021. 14

    2021 arXiv

  57. [65]

    Bonbon alignment for large language models and the sweetness of best-of-n sampling

    Lin Gui, Cristina Gˆarbacea, and Victor Veitch. Bonbon alignment for large language models and the sweetness of best-of-n sampling. arXiv:2406.00832, 2024

    2024 arXiv

  58. [66]

    A new measure of rank correlation.Biometrika, 30(1-2):81–93, 1938

    Maurice G Kendall. A new measure of rank correlation.Biometrika, 30(1-2):81–93, 1938

    1938

  59. [67]

    Evaluating and inducing personality in pre-trained language models

    Guangyuan Jiang, Manjie Xu, Song-Chun Zhu, Wenjuan Han, Chi Zhang, and Yixin Zhu. Evaluating and inducing personality in pre-trained language models. InProceedings of the 36th Annual Conference on Neural Information Processing Systems (NeurIPS), pages 10622–10643, 2023

    2023

  60. [68]

    Survey of cultural awareness in language models: Text and beyond

    Siddhesh Pawar, Junyeong Park, Jiho Jin, Arnav Arora, Junho Myung, Srishti Yadav, Faiz Ghi- fari Haznitrama, Inhwa Song, Alice Oh, and Isabelle Augenstein. Survey of cultural awareness in language models: Text and beyond. arXiv:2411.00860, 2024

    2024 arXiv

  61. [69]

    Randomness, not representation: The unreliability of evaluating cultural alignment in LLMs

    Ariba Khan, Stephen Casper, and Dylan Hadfield-Menell. Randomness, not representation: The unreliability of evaluating cultural alignment in LLMs. arXiv:2503.08688, 2025

    2025 arXiv

  62. [70]

    Advantage-weighted regression: Simple and scalable off-policy reinforcement learning

    Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine. Advantage-weighted regression: Simple and scalable off-policy reinforcement learning. arXiv:1910.00177, 2019

    1910 arXiv

  63. [71]

    ¨Uber den variabilit ¨atsbereich der fourier’schen konstanten von positiven harmonischen funktionen.Rendiconti Del Circolo Matematico di Palermo (1884- 1940), 32(1):193–217, 1911

    Constantin Carath ´eodory. ¨Uber den variabilit ¨atsbereich der fourier’schen konstanten von positiven harmonischen funktionen.Rendiconti Del Circolo Matematico di Palermo (1884- 1940), 32(1):193–217, 1911

    1940

  64. [72]

    On the computational complexity of combinatorial problems.Networks, 5(1): 45–68, 1975

    Richard M Karp. On the computational complexity of combinatorial problems.Networks, 5(1): 45–68, 1975

    1975

  65. [73]

    Springer, 1988

    Martin Gr¨otschel, L´aszl´o Lov´asz, and Alexander Schrijver.Geometric algorithms and combina- torial optimization. Springer, 1988

    1988

  66. [74]

    A theorem on the construction of voting paradoxes.Econometrica: Journal of the Econometric Society, pages 608–610, 1953

    David C McGarvey. A theorem on the construction of voting paradoxes.Econometrica: Journal of the Econometric Society, pages 608–610, 1953

    1953

  67. [75]

    On the density of families of sets.Journal of Combinatorial Theory, Series A, 13(1):145–147, 1972

    Norbert Sauer. On the density of families of sets.Journal of Combinatorial Theory, Series A, 13(1):145–147, 1972

    1972

  68. [76]

    Cambridge university press, 2014

    Shai Shalev-Shwartz and Shai Ben-David.Understanding Machine Learning: From Theory to Algorithms. Cambridge university press, 2014

    2014

  69. [77]

    Soap: Improving and stabilizing Shampoo using Adam

    Nikhil Vyas, Depen Morwani, Rosie Zhao, Mujin Kwun, Itai Shapira, David Brandfonbrener, Lucas Janson, and Sham Kakade. Soap: Improving and stabilizing Shampoo using Adam. In Proceedings of the 13th International Conference on Learning Representations (ICLR), 2025. Forthcoming

    2025

  70. [78]

    Training language models to follow instructions with human feedback

    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. InProceedings of the 35th Annual Conference on Neural Info...

    2022

  71. [79]

    Iterative data smoothing: Mitigating reward overfitting and overoptimization in RLHF

    Banghua Zhu, Michael I Jordan, and Jiantao Jiao. Iterative data smoothing: Mitigating reward overfitting and overoptimization in RLHF. InProceedings of the 41st International Conference on Machine Learning (ICML), pages 62405–62428, 2024

    2024

  72. [80]

    echo chambers,

    Lora Aroyo, Alex Taylor, Mark Diaz, Christopher Homan, Alicia Parrish, Gregory Serapio- Garc´ıa, Vinodkumar Prabhakaran, and Ding Wang. Dices dataset: Diversity in conversational AI evaluation for safety. InProceedings of the 36th Annual Conference on Neural Information Proces...

    2023

  73. [81]

    We index xθ by pairsij withi<j , wherexθ ij =1[r θ(yi)≥r θ(yj)]

    denote its incidence vector. We index xθ by pairsij withi<j , wherexθ ij =1[r θ(yi)≥r θ(yj)]. Observe that, for eachi∈N , their reward vector corresponds to some incidence vector. Thus, the vector x with xij =p ⋆(x,yi,yj) must live in the convex hull of incidence vectors; in p...

  74. [82]

    disagreement score

    is a valid tournament graph (i.e., y∈TG ) is NP-hard. This implies that the potentially harder problem of finding a distribution over rankingsp σ consistent with a givent∈TGis also NP-hard. To establish this, we reduce from the NP-hard problem ofMinimum Feedback Arc Set (MFAS)...

  75. [83]

    Fix m points (z1,t 1),...,(z m,tm)

    Linear combinations:Let F1 ={Pk j=1αj·fj(z)|f j∈F,α j∈R k}, the set of k-sized linear combinations of functions inF. Fix m points (z1,t 1),...,(z m,tm). First, consider the maximum number of sign patterns of z1,...,z m which can be realized by (the binary-valued) F. Since F ha...

  76. [84]

    IfF 2 pseudo-shatters ((z1,y 1),t 1),...,((z m,ym),tm), thenF 1 pseudo-shatters (z1,t 1−y 1),...,(z m,tm−ym), implying that the pseudo-dimension ofF 2 is at most that ofF 1

    Affine shifts:LetF 2 ={(z,y)7→f(z)−y|f∈F 1}. IfF 2 pseudo-shatters ((z1,y 1),t 1),...,((z m,ym),tm), thenF 1 pseudo-shatters (z1,t 1−y 1),...,(z m,tm−ym), implying that the pseudo-dimension ofF 2 is at most that ofF 1. Hence,d 2≤d 1

  77. [85]

    overall” preference. In our experiments, we specifically use the “overall

    Squaring:LetF 3 ={z7→f(z) 2|f∈F 2}. Fixm pointsS={(z 1,t 1),...(z m,tm)} pseudo-shattered byF3. Assume eachti >0 , as otherwise that point alone cannot be shattered. Furthermore assume thatf(zi)̸=t i for anyf∈F 3, otherwise we can adjustti such that the set is still pseudo-sha...

    2008

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.