| Challenge: | Preference optimization methods have been successfully applied to improve the alignment of large language models with human values. |
| Approach: | They propose to use preference optimization methods to generate rejected answers using weak LLM prompting and digit corruption to improve the mathematical reasoning abilities of language models. |
| Outcome: | The proposed method leads to increased accuracy on the GSM8K and AQuA-RAT benchmarks without annotations. |
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Yunqiao Yang, Houxing Ren, Zimu Lu, Ke Wang, Weikang Shi, Aojun Zhou, Junting Pan, Mingjie Zhan, Hongsheng Li
| Challenge: | Recent advances in preference optimization have demonstrated significant potential for improving mathematical reasoning capabilities in large language models. |
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Comparing Bad Apples to Good Oranges Aligning Large Language Models via Joint Preference Optimization (2025.findings-acl)
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| Challenge: | Recent studies have shown that acquiring human preferences by comparing generations is not effective for large language models. |
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RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs (2024.emnlp-main)
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| Challenge: | Preference optimization is a widely adopted post-training technique to align large language models with human preferences. |
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Aligning Large and Small Language Models via Chain-of-Thought Reasoning (2024.eacl-long)
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| Challenge: | Chain-of-Thought (CoT) prompting empowers Large Language Models to solve complex reasoning tasks in a step-wise manner. |
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A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques (2024.acl-long)
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| Challenge: | Large language models are pre-trained on trillions of tokens and instruction-tuned or aligned to specific preferences. |
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Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up (2025.acl-long)
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| Challenge: | Existing methods to improve LLMs’ logical capabilities involve traceable or verifiable logical sequences that generate more reliable responses yet increase computational costs, or introduce rigid logic template rules, reducing flexibility. |
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Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models (2024.emnlp-main)
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| Challenge: | Existing approaches to align reasoning abilities between Large Language Models and Smaller Language Model are supervised fine-tuning and preference optimization. |
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Uncovering Factor-Level Preference to Improve Human-Model Alignment (2025.findings-emnlp)
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| Challenge: | Large language models exhibit tendencies that diverge from human preferences, such as favoring certain writing styles or producing overly verbose outputs. |
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Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning (2024.acl-long)
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| Challenge: | Recent studies focus on enhancing large-scale language models' reasoning abilities, but the research question of how to GSM8K Performance vs. computational cost remains. |
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SeaPO: Strategic Error Amplification for Robust Preference Optimization of Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods for preference optimization of large language models use pairs of positive and negative samples, but the quality of positive samples may become similar during training, complicating preference learning. |
| Approach: | SeaPO introduces error types commonly occurring in large language models to improve preference learning. |
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