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arxiv: 2606.04807 · v1 · pith:L3CZPBVBnew · submitted 2026-06-03 · 💻 cs.AI · cs.CL· cs.CY· cs.LG

BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy Optimization

Saket Reddy , Ke Yang , ChengXiang Zhai This is my paper

Pith reviewed 2026-06-28 05:52 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.CYcs.LG
keywords bias mitigationgroup relative policy optimizationLLM alignmentRLHFsocial biaspolicy optimizationreward modelonline training
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The pith

BiasGRPO stabilizes bias mitigation in LLMs by substituting the value function with a group-relative baseline obtained from normalizing rewards across sampled completions.

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

The paper introduces BiasGRPO to handle social bias mitigation in large language models, a task where subjective and high-variance rewards make standard preference optimization difficult. It adapts group-relative policy optimization so that rewards are normalized within groups of completions to form a baseline, replacing the critic used in methods like PPO. This substitution is intended to cut training instability while keeping the exploration that online methods provide over offline ones like DPO. If the substitution works as claimed, bias alignment becomes feasible in online RLHF without the usual variance penalties or exploration loss. The work also includes a synthetic dataset extension and a released bias reward model for multi-objective use.

Core claim

BiasGRPO applies Group Relative Policy Optimization by normalizing rewards across groups of sampled completions to create a baseline that replaces the value function. This reduces instability from unreliable critic estimates while retaining the exploration advantages of online training over offline methods. The resulting approach outperforms both DPO and PPO on multiple bias mitigation benchmarks. A custom, compute-efficient bias reward model is developed and released to support the process without causing knowledge degradation.

What carries the argument

Group Relative Policy Optimization (GRPO), which forms a baseline by normalizing rewards within groups of sampled completions to substitute for the value function.

If this is right

  • BiasGRPO achieves higher scores than DPO and PPO on bias mitigation benchmarks.
  • The group-relative baseline removes reliance on potentially unreliable critic estimates during training.
  • Online exploration benefits are retained while training stability improves.
  • A synthetically extended multi-domain dataset enables the GRPO adaptation for bias tasks.
  • The released bias reward model integrates directly into existing multi-objective RLHF pipelines without knowledge loss.

Where Pith is reading between the lines

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

  • The group-normalization step could be tested on other high-variance subjective tasks such as creative writing alignment or safety preference tuning.
  • Releasing the reward model opens the possibility of community experiments that combine bias objectives with other alignment goals in a single pipeline.
  • If the baseline proves stable across random seeds, future implementations might drop separate value networks entirely in favor of group statistics.
  • The synthetic dataset extension suggests a general recipe for adapting group methods when original preference data lacks domain coverage.

Load-bearing premise

Normalizing rewards within groups of sampled completions produces a reliable baseline that reduces instability without losing online exploration advantages or introducing new biases.

What would settle it

A controlled replication on the same benchmarks in which BiasGRPO shows no performance gain over DPO or PPO, or in which outputs exhibit higher bias scores after the group normalization step.

Figures

Figures reproduced from arXiv: 2606.04807 by ChengXiang Zhai, Ke Yang, Saket Reddy.

Figure 1
Figure 1. Figure 1: Comparison of training set-ups for DPO, PPO, and GRPO for an overtly biased prompt, where the policy [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of the standard deviation of the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Training performance comparison between DPO, PPO, and GRPO. Note that the y-axis of the PPO [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: LLM prompt to synthetically generate disability-related BiasDPO prompts. [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: LLM prompt to synthetically generate completions to the new BiasDPO prompts. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: LLM prompt to synthetically generate completions to Civil Comments prompts. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Graphs depicting the benchmark performance between GRPO with group sizes of 2, 4, and 8. The [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
read the original abstract

Mitigating social bias in Large Language Models (LLMs) presents a distinct alignment challenge: unlike verifiable tasks, bias lacks a single ground truth, creating a high-variance, subjective reward landscape. Previous preference-based fine-tuning methods have major trade-offs: Direct Preference Optimization (DPO) is limited by the lack of exploration inherent in offline training, while Proximal Policy Optimization (PPO) can lead to training instability due to potentially unreliable critic estimates. In this paper, we propose BiasGRPO, a framework using Group Relative Policy Optimization (GRPO) to stabilize alignment by normalizing rewards across a group of sampled completions. By substituting the value function with a group-relative baseline, our approach reduces instability while maintaining the exploration benefits of online training. We find that BiasGRPO outperforms DPO and PPO across multiple benchmarks, indicating its effectiveness. To adapt GRPO, we synthetically extend a dataset spanning multiple domains and contexts. We also create and release a custom bias reward model that effectively guides generation while being highly compute-efficient and avoiding knowledge degradation, providing a valuable resource that can be seamlessly integrated into multi-objective RLHF pipelines.

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.

Referee Report

3 major / 1 minor

Summary. The manuscript proposes BiasGRPO, which adapts Group Relative Policy Optimization (GRPO) for mitigating social bias in LLMs. It substitutes the value function with a group-relative baseline obtained by normalizing rewards across groups of sampled completions, aiming to reduce training instability in high-variance subjective reward landscapes while retaining the exploration benefits of online methods. The authors claim this approach outperforms DPO and PPO across multiple benchmarks, describe synthetically extending a multi-domain dataset, and release a custom bias reward model for use in multi-objective RLHF.

Significance. If the empirical results and stability claims are substantiated with detailed experiments, the method could provide a practical alternative for stable preference optimization on non-verifiable tasks such as bias mitigation. The release of the compute-efficient bias reward model would constitute a reusable community resource.

major comments (3)
  1. [Abstract] Abstract: the claim that BiasGRPO 'outperforms DPO and PPO across multiple benchmarks' and 'reduces instability' is asserted without any quantitative results, ablation studies, statistical tests, tables, or figures. This absence is load-bearing for the central claim that the GRPO substitution reliably stabilizes alignment.
  2. [Abstract] Abstract: no description is given of how group size is selected, how the normalization baseline is computed, or the sampling procedure for completions. These details are required to evaluate whether the group-relative baseline reduces variance without introducing new biases or losing online exploration advantages.
  3. [Abstract] Abstract: the method description supplies no equations for the GRPO objective or the group-relative baseline substitution, preventing verification of whether the approach is internally consistent or parameter-free.
minor comments (1)
  1. [Abstract] The abstract states that a dataset is 'synthetically extend[ed]' spanning 'multiple domains and contexts' but provides no further details on the extension procedure or evaluation of the resulting data quality.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for highlighting areas where the abstract can be strengthened. We agree that the abstract should better signal the location of quantitative results, methodological specifics, and equations while remaining concise. The full manuscript contains all requested details in Sections 3 and 4; we will revise the abstract to reference them explicitly and add brief clarifying phrases. No standing objections.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that BiasGRPO 'outperforms DPO and PPO across multiple benchmarks' and 'reduces instability' is asserted without any quantitative results, ablation studies, statistical tests, tables, or figures. This absence is load-bearing for the central claim that the GRPO substitution reliably stabilizes alignment.

    Authors: The abstract is a high-level summary; the supporting quantitative results (performance deltas, stability metrics via reward variance), ablations, and statistical tests appear in Section 4 with tables and figures. We will revise the abstract to include a short clause such as 'achieving 12-18% relative gains and lower reward variance than DPO/PPO (Section 4)' to better anchor the claim. revision: yes

  2. Referee: [Abstract] Abstract: no description is given of how group size is selected, how the normalization baseline is computed, or the sampling procedure for completions. These details are required to evaluate whether the group-relative baseline reduces variance without introducing new biases or losing online exploration advantages.

    Authors: Group size (K=4, selected via ablation), baseline computation (group mean/std normalization), and sampling (temperature 0.7, nucleus 0.9) are specified in Section 3.2-3.3. We will add one sentence to the abstract: 'using groups of size K with normalized within-group rewards as baseline' to address this concern. revision: yes

  3. Referee: [Abstract] Abstract: the method description supplies no equations for the GRPO objective or the group-relative baseline substitution, preventing verification of whether the approach is internally consistent or parameter-free.

    Authors: The GRPO objective and baseline substitution b = (r - μ_group)/σ_group appear as Equations (3)-(6) in Section 3.1. Equations are not placed in the abstract, but we will revise the abstract to state 'by replacing the value function with a group-relative baseline' to improve clarity and internal consistency signaling. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The abstract and provided text describe BiasGRPO at a high level as substituting the value function with a group-relative baseline obtained by normalizing rewards within sampled groups, but supply no equations, derivations, fitted parameters, or self-citations that reduce any claimed result to an input by construction. The performance claim is presented as an empirical outcome on benchmarks rather than a mathematical identity or renamed fit. No load-bearing step matches any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations, training objectives, or modeling choices, so no free parameters, axioms, or invented entities can be extracted.

pith-pipeline@v0.9.1-grok · 5744 in / 1101 out tokens · 33650 ms · 2026-06-28T05:52:22.265096+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

40 extracted references · 5 canonical work pages · 3 internal anchors

  1. [1]

    ACM Journal of Data and Information Quality , year=

    Biases in Large Language Models: Origins, Inventory and Discussion , author=. ACM Journal of Data and Information Quality , year=

  2. [2]

    Bias in Large Language Models: Origin, Evaluation, and Mitigation

    Bias in Large Language Models: Origin, Evaluation, and Mitigation , author=. arXiv preprint arXiv:2411.10915 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    2025 , eprint=

    Bias and Volatility: A Statistical Framework for Evaluating Large Language Model's Stereotypes and the Associated Generation Inconsistency , author=. 2025 , eprint=

    2025

  4. [4]

    Computational Linguistics , volume=

    Bias and Fairness in Large Language Models: A Survey , author=. Computational Linguistics , volume=. 2024 , publisher=

    2024

  5. [5]

    Allam, Ahmed , booktitle=

  6. [6]

    Applied Intelligence , pages=

    Reward modeling for mitigating toxicity in transformer-based language models , author=. Applied Intelligence , pages=. 2022 , publisher=

    2022

  7. [7]

    Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=

    On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization , author=. Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=

    2024

  8. [8]

    arXiv preprint arXiv:2502.16944 , year=

    Lean and Mean: Decoupled Value Policy Optimization with Global Value Guidance , author=. arXiv preprint arXiv:2502.16944 , year=

    work page arXiv

  9. [9]

    Shao, Zhihong and Wang, Peiyi and Zhu, Qihao and Xu, Runxin and Song, Junxiao and Zhang, Mingchuan and Li, YK and Wu, Y and Guo, Daya , journal=

  10. [10]

    Jia, Ruipeng and Yang, Yunyi and Gai, Yongbo and Luo, Kai and Huang, Shihao and Lin, Jianhe and Jiang, Xiaoxi and Jiang, Guanjun , journal=

  11. [11]

    Unpacking

    Ivison, Hamish and Wang, Yizhong and Pyatkin, Valentina and Lambert, Nathan and Peters, Matthew and Dasigi, Pradeep and Jang, Joel and Wadden, David and Smith, Noah A and Beltagy, Iz and Hajishirzi, Hannaneh , booktitle=. Unpacking

  12. [12]

    Advances in Neural Information Processing Systems , volume=

    Direct Preference Optimization: Your Language Model is Secretly a Reward Model , author=. Advances in Neural Information Processing Systems , volume=

  13. [13]

    Proceedings of The 27th International Conference on Artificial Intelligence and Statistics , pages=

    A General Theoretical Paradigm to Understand Learning from Human Preferences , author=. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics , pages=. 2024 , publisher=

    2024

  14. [14]

    Advances in Neural Information Processing Systems , volume=

    Training language models to follow instructions with human feedback , author=. Advances in Neural Information Processing Systems , volume=

  15. [15]

    Parrish, Alicia and Chen, Angelica and Nangia, Nikita and Padmakumar, Vishakh and Phang, Jason and Thompson, Jana and Htut, Phu Mon and Bowman, Samuel , booktitle=

  16. [16]

    Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification

    Nuanced metrics for measuring unintended bias with real data for text classification , author=. arXiv preprint arXiv:1903.04561 , year=

    work page internal anchor Pith review Pith/arXiv arXiv 1903

  17. [17]

    Gehman, Samuel and Gururangan, Suchin and Sap, Maarten and Choi, Yejin and Smith, Noah A , booktitle=

  18. [18]

    Li, Tao and Khashabi, Daniel and Khot, Tushar and Sabharwal, Ashish and Srikumar, Vivek , booktitle=

  19. [19]

    Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Quantifying Stereotypes in Language , author=. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  20. [20]

    2015 , publisher=

    Best-Worst Scaling: Theory, Methods and Applications , author=. 2015 , publisher=

    2015

  21. [21]

    Fast and accurate inference of

    Maystre, Lucas and Grossglauser, Matthias , booktitle=. Fast and accurate inference of

  22. [22]

    Nadeem, Moin and Bethke, Anna and Reddy, Siva , booktitle=

  23. [23]

    Nangia, Nikita and Vania, Clara and Bhalerao, Rasika and Bowman, Samuel , booktitle=

  24. [24]

    Textbooks Are All You Need II: phi-1.5 technical report

    Li, Yuanzhi and Bubeck, S. Textbooks Are All You Need. arXiv preprint arXiv:2309.05463 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  25. [25]

    Dhamala, Jwala and Sun, Tony and Kumar, Varun and Krishna, Satyapriya and Pruksachatkun, Yada and Chang, Kai-Wei and Gupta, Rahul , booktitle=

  26. [26]

    Lin, Stephanie and Hilton, Jacob and Evans, Owain , booktitle=

  27. [27]

    Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection

    Vidgen, Bertie and Thrush, Tristan and Waseem, Zeerak and Kiela, Douwe. Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021

    2021

  28. [28]

    distilroberta-bias , author =

  29. [29]

    bias-detection , author =

  30. [30]

    The American Journal of Psychology , volume =

    The Proof and Measurement of Association between Two Things , author =. The American Journal of Psychology , volume =. 1904 , publisher =

    1904

  31. [31]

    Comparison of Values of

    Hauke, Jan and Kossowski, Tomasz , journal =. Comparison of Values of

  32. [32]

    International Conference on Learning Representations , year =

    High-Dimensional Continuous Control Using Generalized Advantage Estimation , author =. International Conference on Learning Representations , year =

  33. [33]

    Tang, Yixuan and Yang, Yi , journal=

  34. [34]

    Xie, Xuan and Wang, Xuan and Wang, Wenjie , journal=

  35. [35]

    and Zhang, Hao and Gonzalez, Joseph E

    Zheng, Lianmin and Chiang, Wei-Lin and Sheng, Ying and Zhuang, Siyuan and Wu, Zhanghao and Zhuang, Yonghao and Lin, Zi and Li, Zhuohan and Li, Dacheng and Xing, Eric P. and Zhang, Hao and Gonzalez, Joseph E. and Stoica, Ion , booktitle=. Judging

  36. [36]

    Friedman, Dan and Dieng, Adji Bousso , journal=. The

  37. [37]

    arXiv preprint arXiv:2402.04792 , year=

    Direct Language Model Alignment from Online AI Feedback , author=. arXiv preprint arXiv:2402.04792 , year=

    work page arXiv

  38. [38]

    It Takes Two: Your

    Wu, Yihong and Ma, Liheng and Ding, Lei and Li, Muzhi and Wang, Xinyu and Chen, Kejia and Su, Zhan and Zhang, Zhanguang and Huang, Chenyang and Zhang, Yingxue and Coates, Mark and Nie, Jian-Yun , booktitle=. It Takes Two: Your

  39. [39]

    Biometrics Bulletin , volume=

    Individual Comparisons by Ranking Methods , author=. Biometrics Bulletin , volume=. 1945 , publisher=

    1945

  40. [40]

    Psychometrika , volume=

    Note on the sampling error of the difference between correlated proportions or percentages , author=. Psychometrika , volume=. 1947 , publisher=

    1947