Design Considerations in Offline Preference-based RL

Alekh Agarwal; Christoph Dann; Teodor V. Marinov

REVIEW 4 major objections 4 minor 21 references

Design Considerations in Offline Preference-based RL

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

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

Pith's one-line read Offline RLHF quality is governed by loss curvature and data coverage, not just the preference loss itself.

desk verdict A genuinely useful conditional theory of loss curvature and coverage in offline RLHF, with a correctable factor-of-two proof bug and a load-bearing realizability assumption that deserves to be stated more honestly. read the letter →

arxiv 2502.06861 v1 pith:7XOBAEY6 submitted 2025-02-08 cs.LG cs.AI

Alekh Agarwal , Christoph Dann , Teodor V. Marinov This is my paper · ORCID

classification cs.LGcs.AI MSC 68T05

keywords offlinepreference-basedRLRLHFDPOIPOdirectalignmentlosscurvaturecoverageKLdivergencebound

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

This paper gives a unified theoretical account of offline preference-based reinforcement learning methods such as DPO, IPO, and SLiC, treating them all as minimizers of a single family of losses over log-likelihood ratios. Its main result bounds how far a learned policy can be from a natural benchmark policy, showing that the gap depends on the curvature of the loss, the coverage of the offline data, and the magnitude of log-likelihood ratios. A sympathetic reading is that the paper explains why the squared loss of IPO tends to be more stable than the logistic loss of DPO, and why normalizing by a reference policy improves the learned policy. The findings matter because they turn scattered empirical observations about RLHF collapse into a quantitative design principle: pick losses with favorable curvature, keep log-likelihood ratios bounded, and collect data with good coverage.

What carries the argument

The central object is the log-likelihood-ratio score $\omega_{\pi,\mu}(x,y,y') = \log \frac{\pi(y|x)}{\mu(y|x)} - \log \frac{\pi(y'|x)}{\mu(y'|x)}$, which converts preference labels into a real-valued margin for a loss $\ell$. The framework defines $L_\mu(\pi) = E[\ell(\omega \cdot \omega_{\pi,\mu})]$, and the benchmark $\pi_\star$ is defined by the realizability assumption that it pointwise minimizes the conditional expected loss. The proof chain uses proper-loss calibration to interpret $\pi_\star$ as the policy corresponding to an underlying reward function, a curvature assumption (Assumption 3.5) to convert excess loss into a bound on the expected squared centered-reward error, a coverage condition (Assumption 3.4) to move that error from the data distribution to the benchmark distribution, and a log-partition analysis to convert the reward error into a KL divergence. The mechanism that carries the argument is the combination of curvature and coverage: without curvature, small population loss does not force the policy's rewards to be close to the benchmark's; without coverage, closeness under the data distribution says nothing about the policy's behavior where it matters.

What would settle it

Train a DPO-style logistic-loss method on a preference dataset while adding a strong KL penalty to the reference policy to keep the log-likelihood ratios $R$ bounded; if the policy still collapses in terms of evaluation preference despite $R$ staying small and $c_\mu$ staying bounded away from zero, the paper's attribution of collapse to a degenerating curvature constant would be refuted. Conversely, if bounded $R$ prevents the collapse, the theory's mechanism is corroborated.

Watch

Extended reading notes

Core claim

The paper's central claim is Theorem 3.6: for any policy $\pi$ that is an $\epsilon$-minimizer of the population loss, the expected KL divergence to the benchmark $\pi_\star$ satisfies $E_x[\mathrm{KL}(\pi_\star(\cdot|x)\,\|\,\pi(\cdot|x))] \le \sqrt{\epsilon/c_\mu} + \sqrt{C\epsilon/c_\mu} + (e^R/2)(C\epsilon/c_\mu)$, under proper-loss, realizability, coverage, and curvature assumptions. In plain terms, the suboptimality of an offline RLHF policy is controlled by the excess loss, the curvature constant $c_\mu$ of the loss around the optimum, the coverage constant $C$ of the data distribution relative to the benchmark policy, and $R$, the magnitude of log-likelihood ratios. The paper argues that this bound unifies existing methods: DPO, IPO, SLiC and variants differ only in their loss $\ell$, their base policy $\mu$, and their policy-class constraint $\Pi$, and these choices enter the bound through $c_\mu$, $\pi_\star$, and $C$. It also shows empirically on a summarization task that squared loss maintains a stable curvature and bounded $R$, while logistic loss drives $R$ large and $c_\mu$ tiny, matching the predicted collapse.

Load-bearing premise

The paper assumes there is a policy $\pi_\star$ inside the policy class that, for every pair of responses, exactly minimizes the expected loss given the preference labels, meaning the data's underlying preference structure must be perfectly expressible by a policy in the class (together with the base policy's log-probabilities); if this fails, $\pi_\star$ is not the intended optimal policy and the bound's meaning changes.

Editorial extensions

If this is right

Methods with strongly convex losses (e.g., squared loss with $c_\mu = 1/2$) inherit tighter KL guarantees than losses whose curvature decays, such as the logistic loss whose effective $c_\mu$ shrinks exponentially as $R$ grows.
Normalizing by a reference policy ($\mu = \pi_{\text{ref}}$) makes the benchmark $\pi_\star$ the KL-regularized reward-maximizing policy, whereas a uniform base policy yields a less desirable benchmark; the bound therefore predicts that reference-policy normalization should improve learned quality, as observed in the experiments.
DPO's observed collapse is explained by the framework: the logistic loss allows log-likelihood ratios to grow without bound, which drives $R$ up and $c_\mu$ down, making the Theorem 3.6 bound vacuous; adding pessimistic or KL-style regularization improves the situation by keeping $R$ in check.
The coverage constant $C$ enters the bound linearly, so offline RLHF guarantees degrade gracefully with data coverage but fail entirely when the data distribution does not cover the benchmark policy's support.
The framework abstracts away reparameterization arguments, so the design principles apply to a broad class of methods (including those with cross-entropy constraints or early-stopping-induced policy classes) rather than only to DPO-style algorithms.

Reading between the lines

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

The bound suggests a practical diagnostic that the authors only hint at: during training, tracking $R$ and an empirical estimate of $c_\mu$ could flag when a method is about to collapse, allowing early stopping or adaptive regularization.
Because the coverage constant $C$ appears explicitly, the framework implies that experimental design for preference data collection—sampling responses under a policy that approximates the intended benchmark—could be as important as the choice of loss, a direction the paper mentions but does not develop.
The realizability assumption is likely the first thing to fail in practice for neural policy classes, since it requires the pointwise minimizer of the conditional loss to be representable as a softmax policy over the same log-probability space; testing this on synthetic BTL data with a linear reward class would clarify how often the bound's premise holds.

Share X Bluesky LinkedIn Reddit HN

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

Short version: this is a real contribution, not a hype paper. It gives one of the first general KL suboptimality bounds for the offline preference-loss family that visibly depends on loss curvature, dataset coverage, and the spread of log-likelihood ratios, and it uses that bound to explain why DPO collapses and IPO does not. The loss-minimization framing itself overlaps with GPO, and IPO already had squared loss, so the novelty is the theorem and the benchmark interpretation, not the recipe.

What it does well: the proper-loss benchmark is a clean way to define what offline direct alignment is aiming at, and the coverage condition is the right generalization of offline RL coverage to this setting. Table 1 is useful. The small TL;DR experiment is consistent with the theory and does show the logistic collapse and the relative stability of squared loss. The citations to GPO, IPO, Swamy et al., and the DPO collapse literature are appropriate; I do not see self-citation inflation.

Soft spots, in order. First, Lemma 4.1 has an off-by-two: it equates the variance of centered reward differences with the pairwise expectation, but pairwise differences have twice the variance. The second and third terms of Theorem 3.6 do not follow with the stated constants. This is fixable, but it has to be fixed before the bound is quoted. Second, and more substantively, Assumption 3.2 is load-bearing and loss-dependent. For squared loss and BTL preferences there is no nonzero reward difference that satisfies the realizability condition, so the pi* benchmark for IPO is not the KL-regularized BTL optimum; it corresponds to a linear preference model. The paper does flag that squared loss is less realistic than BTL, but it still recommends squared loss on the strength of the theory, and the empirical comparison with logistic loss does not actually test the benchmark's realizability. Third, the logistic-loss curvature degradation is stated qualitatively; no formal expression for c_mu as a function of R is given. Fourth, the experiment is a single task with a proprietary judge and no released code; it is corroborating, not decisive.

My take: the conditional theorem is defensible, the proof issues are real but local, and the realizability issue is not hidden, but its implications for the IPO/DPO comparison should be stated more carefully. This deserves serious review, not a desk reject. Recommend: send it out; the right referee should require corrected constants, a formal statement of c_mu for logistic loss, and an explicit discussion of which preference models make Assumption 3.2 plausible. After that revision, it would be a solid paper to cite.

Referee Report

4 major / 4 minor

Summary. The paper studies offline preference-based RL methods (DPO, IPO, SLiC, and variants) from a unified loss-minimization perspective. It defines a benchmark policy pi* whose log-ratio omega_{pi*,mu} pointwise minimizes the expected loss under the preference distribution, imposes a proper-loss condition, a coverage condition, and a curvature condition, and proves (Theorem 3.6) that any approximate minimizer of the population loss is close in expected forward KL to pi*, with the bound controlled by the excess loss, the curvature constant c_mu, the coverage constant C, and a bound R on log-ratio magnitudes. The paper also reports TL;DR experiments comparing logistic (DPO) and squared (IPO) losses with uniform versus reference base policies, finding that the squared loss is more stable and that reference-policy normalization is somewhat helpful.

Significance. If the main technical result is properly repaired, the framework is a useful unifying tool: it avoids the reparameterization argument, covers several practical algorithms, and identifies interpretable quantities that determine policy suboptimality. The explicit caveat in Remark 3.7 about the squared-loss preference model is honest, and the empirical comparison is a reasonable sanity check. The manuscript is not ready in its stated form, however: the proof of the central bound contains a factor-of-2 error in Lemma 4.1, an unjustified logarithmic inequality in Lemma 4.3, and the benchmark for the squared-loss recommendation changes under BTL preferences. With careful constant tracking and a sharper statement of the realizability conditions, the contribution would be solid.

major comments (4)

[Section 4, Lemma 4.1] The equality in Lemma 4.1 is not correct as stated: for i.i.d. y,y' ~ D_y(·|x), E_{x,y,y'}[(F(x,y)−F(x,y'))^2] equals 2 E_{x,y}[(F(x,y)−E_{y|x}F(x,y))^2], not E_{x,y}[∆bar_R(x,y)^2]. With F = R − R*, this means the displayed equality conflates the centered mean-square error with the pairwise difference and is off by a factor of 2. The corrected statement should be E_{x,y}[∆bar_R(x,y)^2] ≤ ϵ/c_mu. This constant propagates: Lemma 4.2 contributes sqrt(2 C E_D[∆bar_R^2]), so the term sqrt(Cϵ/c_mu) in Theorem 3.6 does not follow from the lemmas as written; one obtains sqrt(4Cϵ/c_mu) or, after the correction, sqrt(2Cϵ/c_mu) unless C is redefined. The proof needs to be redone with explicit constant tracking or the theorem restated.
[Section 4, Lemma 4.3] The proof of Lemma 4.3 uses |ln(1+u)| ≤ |u| inside the expectation after bounding E_y exp(∆bar_R) by 1 + E_y ∆bar_R + (e^R/2) E_y ∆bar_R^2. This inequality is false for negative u in (−1,0): for u = −0.5, |ln(1+u)| ≈ 0.693 > 0.5. The argument of the logarithm can be close to zero or negative when E_y ∆bar_R is sufficiently negative, and the passage from the absolute value of the log to the absolute value of the upper bound needs a rigorous justification, such as a lower bound on the partition ratio or an explicit small-ϵ condition. Since the e^R/2 term is used in Remark 3.7 to contrast losses, this gap is load-bearing for the stated bound.
[Section 3.1, Assumption 3.2 and Remark 3.7] For the squared loss, Assumption 3.2 is incompatible with the Bradley-Terry-Luce model that motivates the logistic loss: with g_sq(η) = 2η−1 and η = σ(R*(x,y)−R*(x,y')), realizability forces R*−R*' = 2σ(R*−R*')−1 = tanh((R*−R*')/2), which has only the zero solution. Hence the pi* benchmark in Theorem 3.6 for IPO is not the KL-regularized BTL optimal policy, and the theorem does not by itself justify the empirical comparison in Section 5 through the curvature constant c_mu under the standard BTL model. The paper acknowledges this in Remark 3.7, but the design recommendation that squared loss is ideal needs to be stated as conditional on the corresponding linear preference model, and the experimental section should either use a loss-independent target policy or explicitly test the realizability assumption.
[Section 5, Table 2] The reported curvature constant c_mu = 2.0 for the squared loss ℓ(z) = (βz−1)^2 does not match the theory used to justify it: the strong convexity constant is 2β^2, which equals 0.5 at the stated β = 0.5, not 2.0. As a result, Table 2's claim that the measured c_mu and R are consistent with the theory is not supported by the numbers as reported; the authors should clarify how c_mu was computed and whether the β value used in Table 2 differs from the one reported in the text.

minor comments (4)

[Section 4, Lemma 4.1] The proof refers to 'the second assumption of the lemma' and 'the first condition of the lemma' in a confusing way; these should be rephrased as conditions of the lemma or as Assumption 3.2 and Assumption 3.5.
[Section 3, Assumption 3.5] Assumption 3.5 writes L_mu(pi*_mu; D_xyomega), while the benchmark is denoted pi* elsewhere; the notation should be unified.
[Appendix B] There is a typo in 'groumd-truth' in Appendix B, and Figure 1's caption repeats 'the comparison against pi_ref in Figure 1 (left)' within the same sentence; the presentation should be cleaned up.
[Section 5] When describing the squared-loss experiments, the paper says β = 0.5 but reports c_mu = 2.0; even aside from the constant mismatch, the definition of the loss in the experiments should be written explicitly with the chosen β so the curvature calculation is reproducible.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark is defined through a realizability condition, but its desirability under BTL and the KL bound are derived, not assumed.

full rationale

The derivation chain is self-contained. The benchmark π* is introduced in Assumption 3.2 as the pointwise minimizer of the expected preference loss: "There exists π⋆ ∈ Π such that for all x ∈ X, y∈Y, y′∈Y: ω_{π⋆,μ}(x,y,y′) = argmin_{v∈[−R,R]} E_{Dω}[ℓ(ω·v)|x,y,y′]." This is a compatibility/identifiability condition, not a disguised restatement of the theorem. The paper then gives π* independent normative content: in Section 3.1 and Appendix A, under BTL preferences with the logistic loss, Eq. (3) shows that π* coincides with the KL-regularized reward-maximizing policy π(y|x) ∝ π0(y|x) exp(R*(x,y)), and the text states "When μ = π0, then we see that the policy π⋆ exactly corresponds to this optimal policy." That is a real derivation from the preference model, not a definitional equivalence. Theorem 3.6 is likewise not an identity: it converts excess population loss into KL divergence only through the curvature lower bound (Assumption 3.5), the coverage condition (Assumption 3.4), and the log-partition analysis in Lemmas 4.1–4.3. Without coverage, a global loss minimizer could differ from π* off the data support, so the conclusion is substantive. The only overlapping-author citation, Swamy et al. (2024), motivates the choice of a weaker benchmark but is not used in the proof of Theorem 3.6 and does not carry the derivation. The empirical section measures R and cμ post hoc, but the qualitative prediction that logistic loss curvature degrades as ω grows is a mathematical property of the loss, not a fitted artifact. The paper's own caveat that the squared-loss preference model is "naturally less realistic than say, the BTL model" is a validity concern about Assumption 3.2, not circularity.

Assumptions & free parameters 2 free parameters · 7 assumptions · 0 invented entities

The central bound rests on five numbered assumptions (3.1-3.5) plus an implicit softmax representation of the policy class and an exponential-family preference model in the appendix. No constants are fitted to produce the theorem; the free parameters listed affect only the empirical verification.

free parameters (2)

beta (loss temperature) = beta=0.1 (logistic), beta=0.5 (squared), tuned over {0.1, 0.5, 1.0}
Hyperparameter in the empirical study only; not part of the theoretical bound, but the empirical comparison between losses depends on this tuning.
KL regularization coefficient = 0.005
Added to all experimental variants (Appendix B) to constrain deviation from the reference policy; this regularizer is not analyzed in the theory and could affect the observed stability.

assumptions (7)

domain assumption Assumption 3.1: For all x,y,y' and pi in Pi, |R_pi(x,y)| <= R/4, |log mu(y|x)| <= R/4, and the loss on each sample is bounded by B.
Boundedness ensures the loss inputs live in [-R,R], used throughout the proof to apply curvature and exponential bounds.
domain assumption Assumption 3.2 (Realizability): there exists pi_star in Pi that is the pointwise minimizer of the expected loss E_omega[ell(omega v) | x,y,y'] for all x,y,y'.
Defines the benchmark policy and is required for Lemma 4.1; it is a strong representational condition on the policy class and the data-generating preference process.
standard math Assumption 3.3 (Proper loss): ell is a proper loss for class probability estimation, i.e., argmin_v eta ell(v) + (1-eta) ell(-v) = g_ell(eta).
Standard property of composite losses (Reid and Williamson, 2010); used to connect loss minimizers to probability models, giving pi_star its reward interpretation.
domain assumption Assumption 3.4 (Coverage): there is C such that E_{x,y~pi_star}[Delta R_bar^2] <= C E_{x,y~D}[Delta R_bar^2] for all R in the reward class.
One-sided concentrability condition needed to transfer error bounds from the data distribution to the benchmark policy; standard in offline RL literature.
domain assumption Assumption 3.5 (Curvature around optimum): the expected loss has curvature c_mu at pi_star in the sense of the stated inequality.
Used to convert excess loss into squared reward error; squared loss satisfies it globally with c_mu = 1/2, logistic loss only with a range-dependent constant.
domain assumption The policy class is effectively softmax: pi proportional to exp(R_bar_pi) is used in Lemma 4.2 to relate KL to reward differences.
The KL-to-reward-difference identity in Lemma 4.2 and the log-partition argument in Lemma 4.3 assume the softmax form pi(y|x) proportional to exp(R(x,y)); this is not true for arbitrary policy classes.
domain assumption Preference model lies in an exponential family (Appendix A) when connecting proper losses to reward models.
Appendix A assumes D_omega follows an exponential family to derive the logistic loss from BTL; this underpins the interpretation of pi_star as a reward-following policy, but is not needed for the bound itself.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Design Considerations in Offline Preference-based RL." pith.science (2026). https://pith.science/paper/7XOBAEY6

@misc{pith2026250206861,
  author       = {Pith},
  title        = {Pith review of: Design Considerations in Offline Preference-based RL},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7XOBAEY6}},
  note         = {Machine review of arXiv:2502.06861}
}

read the original abstract

Offline algorithms for Reinforcement Learning from Human Preferences (RLHF), which use only a fixed dataset of sampled responses given an input, and preference feedback among these responses, have gained increasing prominence in the literature on aligning language models. In this paper, we study how the different design choices made in methods such as DPO, IPO, SLiC and many variants influence the quality of the learned policy, from a theoretical perspective. Our treatment yields insights into the choices of loss function, the policy which is used to normalize log-likelihoods, and also the role of the data sampling policy. Notably, our results do not rely on the standard reparameterization-style arguments used to motivate some of the algorithms in this family, which allows us to give a unified treatment to a broad class of methods. We also conduct a small empirical study to verify some of the theoretical findings on a standard summarization benchmark.

Figures

Figures reproduced from arXiv: 2502.06861 by the authors.

**Figure 1.** Left panel shows the preference of the learned policy’s summaries against those from the initial policy πref, as evaluated by a prompted Gemini 1.0 Ultra model. Shaded regions represent 95% error bands. Both the logistic loss variants quickly improve in terms of the preference scores initially, but then suffer a catastrophic collapse. Squared loss improves at a similar rate initially, and remains stable throughout t… view at source ↗

**Figure 2.** Evolution of the log-likelihoods of the preferred response (left) and dispreferred response (right) from the preference dataset across the training process. Both variants of the squared loss decrease the log-likelihoods of both the responses during training, but the decrease is relatively mild. The logistic loss, on the other hand, sends these log-likelihoods crashing sharply, even though the dispreferred responses … view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

21 extracted references · 2 canonical work pages

[1]

Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms

Ahmadian, A., Cremer, C., Gall´e, M., Fadaee, M., Kreutzer, J., Pietquin, O., ¨Ust¨un, A., and Hooker, S. Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms. arXiv preprint arXiv:2402.14740,

arXiv
[3]

A similar line of reasoning shows that when ℓ(v) = (1 − v)2, the link function satisfies η = 1+v⋆ 2 and so the resulting reward model is P(ω|x, y, y′) = 1+R⋆(x,y)−R⋆(x,y′) 2 . B. Experiment details We evaluate the different variants on the TL;DR dataset (V¨olske et al., 2017), where the task is to summarize posts on reddit forums. The dataset consists of ...

work page 2017
[5]

Robust preference optimization through reward model distillation

Fisch, A., Eisenstein, J., Zayats, V ., Agarwal, A., Beirami, A., Nagpal, C., Shaw, P., and Berant, J. Robust preference optimization through reward model distillation. arXiv preprint arXiv:2405.19316,

arXiv
[6]

D., Sun, W., Krish- namurthy, A., and Foster, D

Huang, A., Zhan, W., Xie, T., Lee, J. D., Sun, W., Krish- namurthy, A., and Foster, D. J. Correcting the mythos of kl-regularization: Direct alignment without overopti- mization via chi-squared preference optimization. arXiv preprint arXiv:2407.13399,

arXiv
[7]

Provably mitigating overopti- mization in rlhf: Your sft loss is implicitly an adversarial regularizer

Liu, Z., Lu, M., Zhang, S., Liu, B., Guo, H., Yang, Y ., Blanchet, J., and Wang, Z. Provably mitigating overopti- mization in rlhf: Your sft loss is implicitly an adversarial regularizer. arXiv preprint arXiv:2405.16436,

arXiv
[9]

G., Row- land, M., Guo, Z

Munos, R., Valko, M., Calandriello, D., Azar, M. G., Row- land, M., Guo, Z. D., Tang, Y ., Geist, M., Mesnard, T., Michi, A., et al. Nash learning from human feedback. arXiv preprint arXiv:2312.00886,

arXiv
[11]

Disentan- gling length from quality in direct preference optimiza- tion

9 Offline Learning Preference-based RL Park, R., Rafailov, R., Ermon, S., and Finn, C. Disentan- gling length from quality in direct preference optimiza- tion. arXiv preprint arXiv:2403.19159,

arXiv
[12]

Scaling laws for reward model overoptimization in direct alignment algo- rithms

Rafailov, R., Chittepu, Y ., Park, R., Sikchi, H., Hejna, J., Knox, B., Finn, C., and Niekum, S. Scaling laws for reward model overoptimization in direct alignment algo- rithms. arXiv preprint arXiv:2406.02900, 2024a. Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Er- mon, S., and Finn, C. Direct preference optimization: Your language model is se...

arXiv

Show all 21 references

[15]

M., Hauth, A., et al

Team, G., Anil, R., Borgeaud, S., Wu, Y ., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805,

arXiv
[16]

Is rlhf more difficult than standard rl? arXiv preprint arXiv:2306.14111,

Wang, Y ., Liu, Q., and Jin, C. Is rlhf more difficult than standard rl? arXiv preprint arXiv:2306.14111,

arXiv
[19]

Zhao, Y ., Joshi, R., Liu, T., Khalman, M., Saleh, M., and Liu, P. J. SLiC-HF: Sequence likelihood calibration with human feedback. arXiv preprint arXiv:2305.10425,

arXiv
[32]

The optimizer used is Adafactor with learning rate that is constant with a linear warm-up for 2000 steps and a base rate of 1e −

A KL regularizer is used to the reference πref checkpoint with coefficient equal to 0.005. The optimizer used is Adafactor with learning rate that is constant with a linear warm-up for 2000 steps and a base rate of 1e −

2000
[1952]

Value-incentivized preference optimization: A unified approach to online and offline rlhf

Cen, S., Mei, J., Goshvadi, K., Dai, H., Yang, T., Yang, S., Schuurmans, D., Chi, Y ., and Dai, B. Value-incentivized preference optimization: A unified approach to online and offline rlhf. arXiv preprint arXiv:2405.19320,

arXiv
[2007]

Zhao, Y ., Khalman, M., Joshi, R., Narayan, S., Saleh, M., and Liu, P. J. Calibrating sequence likelihood im- proves conditional language generation. arXiv preprint arXiv:2210.00045,

arXiv
[2010]

On regularization via early stopping for least squares regression

Sonthalia, R., Lok, J., and Rebrova, E. On regularization via early stopping for least squares regression. arXiv preprint arXiv:2406.04425,

arXiv
[2012]

URL https://books

ISBN 9780486153391. URL https://books. google.com/books?id=ERQsKkPiKkkC. Meng, Y ., Xia, M., and Chen, D. SimPO: Simple preference optimization with a reference-free reward. arXiv preprint arXiv:2405.14734,

arXiv
[2017]

KTO: Model alignment as prospect theoretic optimization

Ethayarajh, K., Xu, W., Muennighoff, N., Jurafsky, D., and Kiela, D. KTO: Model alignment as prospect theoretic optimization. arXiv preprint arXiv:2402.01306,

arXiv
[2018]

S., and Agarwal, A

Swamy, G., Dann, C., Kidambi, R., Wu, Z. S., and Agarwal, A. A minimaximalist approach to reinforcement learning from human feedback. arXiv preprint arXiv:2401.04056,

arXiv
[2021]

Xu, H., Sharaf, A., Chen, Y ., Tan, W., Shen, L., Van Durme, B., Murray, K., and Kim, Y . J. Contrastive preference optimization: Pushing the boundaries of llm performance in machine translation. arXiv preprint arXiv:2401.08417,

arXiv
[2022]

Smaug: Fixing failure modes of pref- erence optimisation with dpo-positive

Pal, A., Karkhanis, D., Dooley, S., Roberts, M., Naidu, S., and White, C. Smaug: Fixing failure modes of pref- erence optimisation with dpo-positive. arXiv preprint arXiv:2402.13228,

arXiv
[2024]

Direct pref- erence optimization with an offset

Amini, A., Vieira, T., and Cotterell, R. Direct pref- erence optimization with an offset. arXiv preprint arXiv:2402.10571,

arXiv

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.

[1] [1]

Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms

Ahmadian, A., Cremer, C., Gall´e, M., Fadaee, M., Kreutzer, J., Pietquin, O., ¨Ust¨un, A., and Hooker, S. Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms. arXiv preprint arXiv:2402.14740,

arXiv

[2] [3]

A similar line of reasoning shows that when ℓ(v) = (1 − v)2, the link function satisfies η = 1+v⋆ 2 and so the resulting reward model is P(ω|x, y, y′) = 1+R⋆(x,y)−R⋆(x,y′) 2 . B. Experiment details We evaluate the different variants on the TL;DR dataset (V¨olske et al., 2017), where the task is to summarize posts on reddit forums. The dataset consists of ...

work page 2017

[3] [5]

Robust preference optimization through reward model distillation

Fisch, A., Eisenstein, J., Zayats, V ., Agarwal, A., Beirami, A., Nagpal, C., Shaw, P., and Berant, J. Robust preference optimization through reward model distillation. arXiv preprint arXiv:2405.19316,

arXiv

[4] [6]

D., Sun, W., Krish- namurthy, A., and Foster, D

Huang, A., Zhan, W., Xie, T., Lee, J. D., Sun, W., Krish- namurthy, A., and Foster, D. J. Correcting the mythos of kl-regularization: Direct alignment without overopti- mization via chi-squared preference optimization. arXiv preprint arXiv:2407.13399,

arXiv

[5] [7]

Provably mitigating overopti- mization in rlhf: Your sft loss is implicitly an adversarial regularizer

Liu, Z., Lu, M., Zhang, S., Liu, B., Guo, H., Yang, Y ., Blanchet, J., and Wang, Z. Provably mitigating overopti- mization in rlhf: Your sft loss is implicitly an adversarial regularizer. arXiv preprint arXiv:2405.16436,

arXiv

[6] [9]

G., Row- land, M., Guo, Z

Munos, R., Valko, M., Calandriello, D., Azar, M. G., Row- land, M., Guo, Z. D., Tang, Y ., Geist, M., Mesnard, T., Michi, A., et al. Nash learning from human feedback. arXiv preprint arXiv:2312.00886,

arXiv

[7] [11]

Disentan- gling length from quality in direct preference optimiza- tion

9 Offline Learning Preference-based RL Park, R., Rafailov, R., Ermon, S., and Finn, C. Disentan- gling length from quality in direct preference optimiza- tion. arXiv preprint arXiv:2403.19159,

arXiv

[8] [12]

Scaling laws for reward model overoptimization in direct alignment algo- rithms

Rafailov, R., Chittepu, Y ., Park, R., Sikchi, H., Hejna, J., Knox, B., Finn, C., and Niekum, S. Scaling laws for reward model overoptimization in direct alignment algo- rithms. arXiv preprint arXiv:2406.02900, 2024a. Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Er- mon, S., and Finn, C. Direct preference optimization: Your language model is se...

arXiv