Eliminating Inductive Bias in Reward Models with Information-Theoretic Guidance

Anningzhe Gao; Erchao Zhao; Feifei Tong; Guanjun Jiang; Pengyu Cheng; Tsung-Hui Chang; Xiang Wan; Xiaoxi Jiang; Zhechao Yu; Zhuo Li

arxiv: 2512.23461 · v2 · pith:R7DH4ANRnew · submitted 2025-12-29 · 💻 cs.LG · cs.AI

Eliminating Inductive Bias in Reward Models with Information-Theoretic Guidance

Zhuo Li , Pengyu Cheng , Zhechao Yu , Feifei Tong , Anningzhe Gao , Tsung-Hui Chang , Xiang Wan , Erchao Zhao

show 2 more authors

Xiaoxi Jiang Guanjun Jiang

This is my paper

Pith reviewed 2026-05-21 16:47 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords reward modelsRLHFdebiasingmutual informationinductive biasinformation bottleneckpreference learning

0 comments

The pith

Reward models can remove complex inductive biases such as response length and sycophancy by maximizing mutual information with human preferences while minimizing it with biased input attributes.

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

The paper presents DIR as a way to train reward models that avoid overfitting to superficial features in preference data. It draws on the information bottleneck idea to keep signals tied to actual human choices while stripping out correlations with unwanted attributes. Earlier debiasing methods handled only linear links between biases and outputs, but this approach targets non-linear ones as well. If it works, aligned language models would generalize better during RLHF without falling into reward hacking on details like response length.

Core claim

DIR maximizes the mutual information between reward model scores and human preference pairs while minimizing the mutual information between reward model outputs and biased attributes such as response length, sycophancy indicators, and format features. Theoretical justification from information theory supports its use on sophisticated biases with non-linear correlations, extending debiasing beyond prior linear methods like Pearson coefficients.

What carries the argument

The dual mutual information objective that keeps task-relevant preference information while discarding information about identified biased attributes, drawn from the information bottleneck principle.

If this is right

DIR reduces the effects of response length, sycophancy, and format biases in trained reward models.
Models aligned with DIR show higher performance on diverse RLHF benchmarks.
The approach produces reward models with stronger generalization to new inputs.
It broadens debiasing techniques to handle non-linear bias correlations in real training data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

DIR could be tested on additional bias types that emerge in new preference datasets without requiring changes to the core objective.
The method suggests a route to combine information-theoretic debiasing with other regularization techniques for layered bias control.
If bias identification improves, DIR might reduce reliance on manual data filtering steps in reward model pipelines.

Load-bearing premise

Biased attributes can be reliably identified in advance and that removing their mutual information with model outputs will preserve all useful preference signals without creating fresh unintended biases.

What would settle it

An experiment that measures the drop in mutual information between reward outputs and target bias attributes after DIR training, while checking whether preference prediction accuracy on held-out data stays the same or rises.

read the original abstract

Reward models (RMs) are essential in reinforcement learning from human feedback (RLHF) to align large language models (LLMs) with human values. However, RM training data is commonly recognized as low-quality, containing inductive biases that can easily lead to overfitting and reward hacking. For example, more detailed and comprehensive responses are usually human-preferred but with more words, leading response length to become one of the inevitable inductive biases. A limited number of prior RM debiasing approaches either target a single specific type of bias or model the problem with only simple linear correlations, \textit{e.g.}, Pearson coefficients. To mitigate more complex and diverse inductive biases in reward modeling, we introduce a novel information-theoretic debiasing method called \textbf{D}ebiasing via \textbf{I}nformation optimization for \textbf{R}M (DIR). Inspired by the information bottleneck (IB), we maximize the mutual information (MI) between RM scores and human preference pairs, while minimizing the MI between RM outputs and biased attributes of preference inputs. With theoretical justification from information theory, DIR can handle more sophisticated types of biases with non-linear correlations, broadly extending the real-world application scenarios for RM debiasing methods. In experiments, we verify the effectiveness of DIR with three types of inductive biases: \textit{response length}, \textit{sycophancy}, and \textit{format}. We discover that DIR not only effectively mitigates target inductive biases but also enhances RLHF performance across diverse benchmarks, yielding better generalization abilities. The code and training recipes are available at https://github.com/Qwen-Applications/DIR.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

DIR uses an IB-style objective to target non-linear biases in reward models across length, sycophancy, and format, with experiments showing bias reduction and RLHF gains, but the approach still depends on upfront bias attribute identification and stable MI estimation.

read the letter

DIR takes the information bottleneck idea and applies it to reward model training to cut out inductive biases like response length, sycophancy, and format preferences. The core move is to maximize mutual information between the RM scores and the actual human preference pairs while minimizing the MI between the RM outputs and these known bias attributes. This is meant to handle non-linear correlations that earlier linear debiasing methods could not touch. The experiments look solid on the surface. They run DIR on three bias types and find that it reduces the target biases while also lifting performance on several RLHF benchmarks. Releasing the code and training recipes is helpful for anyone who wants to reproduce or build on it. The main limitations sit in the practical assumptions. The method needs the biased attributes to be explicitly identified and extracted first, which works for obvious things like length but gets harder for subtler biases. If those attributes still carry some task-relevant signal, minimizing their MI could throw away useful information. MI estimation itself is tricky in the high-dimensional spaces of LLM representations, and any variance there could make the debiasing unstable. The trade-off parameter between the two MI terms also has to be chosen, and if it's tuned on the preference data it introduces some dependence on empirical choices. This is aimed at researchers doing RLHF and alignment work who run into reward hacking from low-quality training signals. Someone looking for a way to regularize reward models beyond simple linear corrections would find the multi-bias results useful. I would send it to peer review. The idea is timely and the experiments show promise, though a referee could usefully press on the MI estimator details and whether the gains hold when bias identification is less clean.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes DIR, an information-bottleneck-inspired debiasing technique for reward models in RLHF. It maximizes mutual information between RM scores and human preference pairs while minimizing mutual information between RM outputs and explicitly identified biased attributes (response length, sycophancy indicators, and format), claiming this removes both linear and non-linear inductive biases. Theoretical motivation from information theory is asserted, and experiments on three bias types report reduced bias and improved RLHF generalization across benchmarks, with code released.

Significance. If the MI objectives can be shown to reliably isolate non-linear bias correlations without discarding preference-relevant information, the method would extend existing linear debiasing approaches and strengthen robustness in reward modeling. The open code and training recipes are a clear strength for reproducibility.

major comments (3)

[§3] §3 (DIR objective): the claim that minimizing I(RM output; bias attr) removes non-linear correlations while preserving task-relevant information lacks an explicit derivation or bound showing that the chosen bias attributes are exhaustive and statistically independent of preference signals once the objective is optimized; this is load-bearing for the extension beyond linear methods asserted in the Abstract.
[Method and Experiments] Method and Experiments sections: mutual information estimation for high-dimensional RM outputs or hidden states is not specified (e.g., which estimator, sample complexity, or variance control), yet the non-linear bias-handling claim and the reported gains on sycophancy/format biases rest on stable estimation; inaccurate estimators would reduce the approach to a heuristic regularizer.
[Experimental setup] Experimental setup (trade-off parameter): the balancing parameter beta between the two MI terms is a free hyperparameter whose selection procedure is not detailed; if tuned on the same preference data used for evaluation, this introduces circularity that undermines the generalization claims.

minor comments (2)

[Abstract] Abstract: the statement that DIR 'broadly extending the real-world application scenarios' would benefit from a brief qualifier on the scope of biases considered.
[Method] Notation: consistent use of symbols for RM outputs versus scores would improve readability when describing the two MI terms.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the theoretical and methodological aspects of our work. We address each major point below, indicating planned revisions where appropriate to improve the manuscript's rigor and transparency.

read point-by-point responses

Referee: [§3] §3 (DIR objective): the claim that minimizing I(RM output; bias attr) removes non-linear correlations while preserving task-relevant information lacks an explicit derivation or bound showing that the chosen bias attributes are exhaustive and statistically independent of preference signals once the objective is optimized; this is load-bearing for the extension beyond linear methods asserted in the Abstract.

Authors: We agree that a more explicit derivation or bound would strengthen the theoretical motivation. In the revised manuscript, we will expand the discussion in §3 to include additional analysis of the information bottleneck assumptions, clarifying the conditions under which the selected bias attributes (length, sycophancy indicators, format) become approximately independent of preference signals post-optimization. We will also explicitly note the limitations regarding exhaustiveness of these attributes and that while empirical results support extension beyond linear debiasing, a complete formal bound remains challenging and is left for future theoretical work. This will temper the Abstract claims accordingly while preserving the practical contributions. revision: yes
Referee: [Method and Experiments] Method and Experiments sections: mutual information estimation for high-dimensional RM outputs or hidden states is not specified (e.g., which estimator, sample complexity, or variance control), yet the non-linear bias-handling claim and the reported gains on sycophancy/format biases rest on stable estimation; inaccurate estimators would reduce the approach to a heuristic regularizer.

Authors: This point is well-taken and highlights a gap in the current presentation. The manuscript will be revised to specify the mutual information estimator (a variational neural estimator based on the MINE framework), including implementation details, batch size considerations for sample complexity, and variance control via moving averages and multiple runs. We will add a dedicated subsection or appendix entry describing these choices and include sensitivity experiments showing that the reported bias reductions and RLHF gains remain stable under reasonable estimator variations. revision: yes
Referee: [Experimental setup] Experimental setup (trade-off parameter): the balancing parameter beta between the two MI terms is a free hyperparameter whose selection procedure is not detailed; if tuned on the same preference data used for evaluation, this introduces circularity that undermines the generalization claims.

Authors: We acknowledge the risk of circularity if beta were tuned directly on evaluation data. In the revision, we will explicitly describe the hyperparameter selection process: beta was chosen via grid search on a held-out validation split of the preference data, disjoint from the test benchmarks used for final reporting. We will report the explored range, selection criterion (validation performance on bias metrics and downstream RLHF reward), and include the chosen value in the experimental setup section to ensure full reproducibility and eliminate concerns about data leakage. revision: yes

Circularity Check

0 steps flagged

DIR objective is independently formulated from IB principle with no reduction to inputs by construction

full rationale

The paper proposes maximizing I(RM scores; human preferences) while minimizing I(RM outputs; biased attributes) as a direct application of the information bottleneck, with experiments on length, sycophancy and format biases. No equations or steps in the provided description reduce a claimed prediction or uniqueness result to a fitted parameter or self-citation by construction. The central derivation remains self-contained: the information-theoretic objective is stated as an extension beyond linear methods, supported by external IB literature and empirical verification rather than tautological re-labeling of inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on standard information-theoretic identities and the assumption that biased attributes can be explicitly measured or approximated from inputs. No new physical entities are postulated.

free parameters (1)

trade-off parameter beta
Balances the maximization of MI with preferences against minimization of MI with biased attributes; must be chosen or tuned.

axioms (1)

domain assumption Mutual information can be reliably estimated or optimized in high-dimensional neural network outputs
Invoked when applying the IB-inspired objective to RM scores.

pith-pipeline@v0.9.0 · 5856 in / 1163 out tokens · 38753 ms · 2026-05-21T16:47:55.858161+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

max I(1y≻ȳ; x,y,ȳ) − λ·I(1y≻ȳ; b) … estimated with Barber-Agakov lower bound and CLUB upper bound
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

response length, sycophancy, format biases … relative bias attributes … variational classifier qψ(b|H)

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

6 extracted references · 6 canonical work pages

[1]

over-correction

URLhttps://arxiv.org/abs/1908.10763. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding, 2021. URLhttps://arxiv.org/abs/2009. 03300. R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. Lea...

work page doi:10.18653/v1/2024.findings-acl.297 1908
[2]

Assistant A is significantly better: [[A>>B]]

work page
[3]

Assistant A is slightly better: [[A>B]]

work page
[4]

Tie, relatively the same: [[A=B]]

work page
[5]

Assistant B is slightly better: [[B>A]]

work page
[6]

My final verdict is tie: [[A=B]]

Assistant B is significantly better: [[B>>A]] Example output: “My final verdict is tie: [[A=B]]” [User Prompt] [Assistant A’s Answer] {answer A} [Assistant B’s Answer] {answer B} D. Case Study Our model demonstrates its ability to generate not just a correct, but a more professionally helpful response com- paredtostrongbaselineslikeGPT-4o. AsvisualizedinF...

work page 2008

[1] [1]

over-correction

URLhttps://arxiv.org/abs/1908.10763. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding, 2021. URLhttps://arxiv.org/abs/2009. 03300. R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. Lea...

work page doi:10.18653/v1/2024.findings-acl.297 1908

[2] [2]

Assistant A is significantly better: [[A>>B]]

work page

[3] [3]

Assistant A is slightly better: [[A>B]]

work page

[4] [4]

Tie, relatively the same: [[A=B]]

work page

[5] [5]

Assistant B is slightly better: [[B>A]]

work page

[6] [6]

My final verdict is tie: [[A=B]]

Assistant B is significantly better: [[B>>A]] Example output: “My final verdict is tie: [[A=B]]” [User Prompt] [Assistant A’s Answer] {answer A} [Assistant B’s Answer] {answer B} D. Case Study Our model demonstrates its ability to generate not just a correct, but a more professionally helpful response com- paredtostrongbaselineslikeGPT-4o. AsvisualizedinF...

work page 2008