PIPA: Preference Alignment as Prior-Informed Statistical Estimation
On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods
Abstract (arXiv)
Offline preference alignment for language models such as Direct Preference Optimization (DPO) is favored for its effectiveness and simplicity, eliminating the need for costly reinforcement learning. Various offline algorithms have been developed for different data settings, yet they lack a unified understanding. In this study, we introduce Pior-Informed Preference Alignment (PIPA), a unified, RL-free probabilistic framework that formulates language model preference alignment as a Maximum Likelihood Estimation (MLE) problem with prior constraints. This method effectively accommodates both paired and unpaired data, as well as answer and step-level annotations. We illustrate that DPO and KTO are special cases with different prior constraints within our framework. By integrating different types of prior information, we developed two variations of PIPA: PIPA-M and PIPA-N. Both algorithms demonstrate a 3~10% performance enhancement on the GSM8K and MATH benchmarks across all configurations, achieving these gains without additional training or computational costs compared to existing algorithms.
Key facts. Cited by 1 paper 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 42 of its 51 references that are in the map. First posted on arXiv 9 Feb 2025.
Builds on
42 of 51 references.
Closest ancestors · 7
Same direction, newest first
- 2024Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization11 Oct
- 2024TIS-DPO: Token-level Importance Sampling for Direct Preference Optimization With Estimated Weights6 Oct
- 2024SimPO: Simple Preference Optimization with a Reference-Free Reward23 May
- 2024Token-level Direct Preference Optimization18 Apr
- 2024Generalized Preference Optimization: A Unified Approach to Offline Alignment8 Feb
- 2023A General Theoretical Paradigm to Understand Learning from Human Preferences18 Oct
- 2022Training language models to follow instructions with human feedback4 Mar
Other references · 35
- 2023GPT-4 Technical Reportcited by 18,396
- 2017Proximal Policy Optimization Algorithmscited by 15,215
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- 2021Training Verifiers to Solve Math Word Problemscited by 10,109
- 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
- 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
- 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
- 2023Let's Verify Step by Stepcited by 3,789
- 2024Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvementcited by 1,090
- 2025UI-TARS: Pioneering Automated GUI Interaction with Native Agentscited by 610
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- 2024DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligencecited by 331
- 2025rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinkingcited by 313
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- 2024Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMscited by 273
- 2024RLHF Workflow: From Reward Modeling to Online RLHFcited by 267
- 2024Iterative Reasoning Preference Optimizationcited by 246
- 2024AlphaMath Almost Zero: Process Supervision without Processcited by 162
- 2024DPO Meets PPO: Reinforced Token Optimization for RLHFcited by 127
- 2020A Distributional Approach to Controlled Text Generationcited by 119
- 2024Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizercited by 109
- 2022On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgettingcited by 82
- 2024Step-level Value Preference Optimization for Mathematical Reasoningcited by 72
- 2024Offline Reinforcement Learning for LLM Multi-Step Reasoningcited by 54
- 2024Step-Controlled DPO: Leveraging Stepwise Error for Enhanced Mathematical Reasoningcited by 35
- 2023A density estimation perspective on learning from pairwise human preferencescited by 23
- 2024Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimizationcited by 22
- 2025Step-KTO: Optimizing Mathematical Reasoning through Stepwise Binary Feedbackcited by 22
- 2019Distributional Reinforcement Learning for Energy-Based Sequential Modelscited by 17
- 2024MARIO Eval: Evaluate Your Math LLM with your Math LLM--A mathematical dataset evaluation toolkitcited by 14
- 2024BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedbackcited by 7
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