mlsanityData to 14 Oct 2026

Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards

arXiv 2510.01167 ↗First posted 1 Oct 2025cs.LG cs.AI cs.CLcited by 6

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

Aligning large language models to human preferences is inherently multidimensional, yet most pipelines collapse heterogeneous signals into a single objective. We seek to answer what it would take to simultaneously align a model across various domains spanning those with: verifiable rewards, non-verifiable subjective preferences, and complex interactive scenarios. Such multi-objective alignment setups are often plagued by individual objectives being at odds with each other, resulting in inefficient training and limited user control during inference. To address these issues, we propose Multi-Action-Head ALignment with PRM-guided DecOding (MAHALO), a unified framework that standardizes PRM training across verifiable and non-verifiable settings for step-level supervision, performs vectorized multi-objective alignment with Multi-Action-Head DPO, and enables controllable inference through objective-specific weighting and PRM-guided decoding. Experiments across math reasoning, human values alignment, and multi-turn tutoring show that MAHALO jointly improves multiple objectives simultaneously with limited interference, while remaining generalizable and adaptable across domains and offering flexible user control at inference time. Our code is available at: https://github.com/pearls-lab/multiobj-align.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 68 of its 84 references that are in the map. First posted on arXiv 1 Oct 2025.

Builds on

68 of 84 references.

Closest ancestors · 12

Same direction, newest first

  1. 2025PARM: Multi-Objective Test-Time Alignment via Preference-Aware Autoregressive Reward Model6 May
  2. 2025Self-Improvement Towards Pareto Optimality: Mitigating Preference Conflicts in Multi-Objective Alignment20 Feb
  3. 2024GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignment10 Oct
  4. 2024Decoding-Time Language Model Alignment with Multiple Objectives27 Jun
  5. 2024Cascade Reward Sampling for Efficient Decoding-Time Alignment24 Jun
  6. 2024MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models25 Mar
  7. 2024Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment29 Feb
  8. 2024Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment15 Feb
  9. 2024ARGS: Alignment as Reward-Guided Search23 Jan
  10. 2023Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization5 Oct
  11. 2022Training language models to follow instructions with human feedback4 Mar
  12. 2021A General Language Assistant as a Laboratory for Alignment1 Dec

Other references · 56

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  4. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  5. 2024GPT-4o System Cardcited by 4,588
  6. 2017Deep reinforcement learning from human preferencescited by 3,912
  7. 2023Let's Verify Step by Stepcited by 3,789
  8. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  9. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  10. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  11. 2017Hindsight Experience Replaycited by 1,136
  12. 2024OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problemscited by 1,136
38 more
  1. 2022Red Teaming Language Models with Language Modelscited by 1,026
  2. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotationscited by 928
  3. 2022Solving math word problems with process- and outcome-based feedbackcited by 901
  4. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedcited by 729
  5. 2023RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignmentcited by 686
  6. 2023Safe RLHF: Safe Reinforcement Learning from Human Feedbackcited by 656
  7. 2023RRHF: Rank Responses to Align Language Models with Human Feedback without tearscited by 463
  8. 2024Improve Mathematical Reasoning in Language Models by Automated Process Supervisioncited by 441
  9. 2025The Lessons of Developing Process Reward Models in Mathematical Reasoningcited by 415
  10. 2023UltraFeedback: Boosting Language Models with Scaled AI Feedbackcited by 406
  11. 2023Fine-Grained Human Feedback Gives Better Rewards for Language Model Trainingcited by 401
  12. 2024Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Expertscited by 376
  13. 2024ProcessBench: Identifying Process Errors in Mathematical Reasoningcited by 292
  14. 2023Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Mergingcited by 280
  15. 2024Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoningcited by 279
  16. 2024RLHF Workflow: From Reward Modeling to Online RLHFcited by 267
  17. 2023Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewardscited by 243
  18. 2024HelpSteer2: Open-source dataset for training top-performing reward modelscited by 236
  19. 2023How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Compositioncited by 223
  20. 2023Evaluating the Performance of Large Language Models on GAOKAO Benchmarkcited by 186
  21. 2023HelpSteer: Multi-attribute Helpfulness Dataset for SteerLMcited by 142
  22. 2023Controlled Decoding from Language Modelscited by 135
  23. 2023MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problemscited by 131
  24. 2025Reward-Guided Speculative Decoding for Efficient LLM Reasoningcited by 106
  25. 2023Eliciting Human Preferences with Language Modelscited by 92
  26. 2019Deep Reinforcement Learning for Multi-objective Optimizationcited by 90
  27. 2019Hindsight Credit Assignmentcited by 79
  28. 2023Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Modelcited by 79
  29. 2024Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutorscited by 61
  30. 2023Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decodingcited by 60
  31. 2024PAD: Personalized Alignment of LLMs at Decoding-Timecited by 60
  32. 2023Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspectivecited by 51
  33. 2024Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMscited by 48
  34. 2022Automatic Generation of Socratic Subquestions for Teaching Math Word Problemscited by 43
  35. 2024Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoningcited by 40
  36. 2024Entropy-Regularized Process Reward Modelcited by 38
  37. 2024AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrailscited by 31
  38. 2025StepWiser: Stepwise Generative Judges for Wiser Reasoningcited by 28

50 most cited of 56 All 56 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2025Multi-Agent Collaborative Filtering: Orchestrating Users and Items for Agentic Recommendationscited by 10
  2. 2026Focal Reward: Balanced Reinforcement Learning under Rubric-Based Rewardscited by 4
  3. 2026Uncovering Cross-Objective Interference in Multi-Objective Alignmentcited by 2
  4. 2026Scaling Participation in Modular AI Systemscited by 2
  5. 2026Many Preferences, Few Policies: Towards Scalable Language Model Personalizationcited by 0
  6. 2026SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Frontcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Reward-free Alignment for Conflicting Objectives
  2. 2026MATO: Multi-objective Personalized Alignment with Test-time Optimization for Large Language Models
  3. 2025Preference Orchestrator: Prompt-Aware Multi-Objective Alignment for Large Language Models
  4. 2026Meta-Aligner: Bidirectional Preference-Policy Optimization for Multi-Objective LLMs Alignment
  5. 2025MAVIS: Multi-Objective Alignment via Inference-Time Value-Guided Selection
  6. 2025REWARD CONSISTENCY: Improving Multi-Objective Alignment from a Data-Centric Perspective
  7. 2025UC-MOA: Utility-Conditioned Multi-Objective Alignment for Distributional Pareto-Optimality
  8. 2025OrthAlign: Orthogonal Subspace Decomposition for Non-Interfering Multi-Objective Alignment