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Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories

Paper · 2026 · Peiyang Liu, Zhirui Chen, Xi Wang et al.

Summary

Search-based data generation with Monte Carlo Tree Search (MCTS) usually keeps only the best-scoring solution path and throws away the rest. The authors argue this wastes useful signal, because failed or inefficient branches show where a model tends to go wrong. They propose Contrastive Reasoning Path Synthesis (CRPS), which splits the work between two models. An explorer model runs MCTS to produce many candidate reasoning trajectories. A stronger analyst model then compares successful and unsuccessful attempts and writes out why one works and the other does not, both for overall strategy and for individual steps. Using these critiques, the analyst writes new reasoning chains that avoid the diagnosed mistakes. These chains are checked for answer correctness before they are used for fine-tuning. With about 60K synthesized examples, fine-tuned models match or beat baselines trained on 590K rejection-sampled examples. They also transfer better to out-of-domain benchmarks. The work suggests that denser, contrast-based supervision can replace much of the brute-force scaling of training data.

Key points

  • Pair selection: Trajectories receive a binary correctness reward. The positive anchor is the shortest correct path, with ties broken by the highest node Q-value. MCTS visit counts help locate incorrect paths the explorer strongly favored.
  • Negative types: Negatives are split into hard ones (wrong answer) and soft ones (correct but strategically inefficient).
  • Data efficiency: 60K CRPS examples reach the performance of 590K rejection-sampling (RFT-style) examples, roughly a 20× reduction.
  • Training cost: With 30K examples, fine-tuning time drops from 192 to 10 GPU hours. The upfront synthesis cost pays off after about two trained model variants.
  • Scope and limitations:
    • Target models are in the 7B–8B range (DeepSeekMath, LLaMA-3).
    • Experiments go beyond math to code generation and commonsense reasoning.
    • The method needs verifiable rewards and runs 10 MCTS rollouts per problem.
    • It relies on a capable analyst, which may mean proprietary APIs, and that analyst can hallucinate critiques.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Peking UniversityBeijing · China244 works · 39,359 citations🏢TencentShenzhen · China134 works · 19,743 citations🎓University of Chinese Academy of SciencesBeijing · China147 works · 8,480 citations🎓Beijing University of TechnologyBeijing · China13 works · 1,910 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Peiyang Liu, Zhirui Chen, Xi Wang, Di Liang, Youru Li, Zhi Cai and Wei Ye. 2026. Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.501

@inproceedings{liu2026learning,
  title = {{Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories}},
  author = {Peiyang Liu, Zhirui Chen, Xi Wang, Di Liang, Youru Li, Zhi Cai and Wei Ye},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.501},
  url = {https://doi.org/10.18653/v1/2026.acl-long.501}
}

References

85 references, 29 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Haonan Dong, Kehan Jiang, Haoran Ye, Wenhao Zhu, Zhaolu Kang, and Guojie Song. 2026. Neureasoner: Towards explainable, controllable, and unified reasoning via mixture-of-neurons. arXiv preprint arXiv:2604.02972.
  2. David Brandfonbrener, Simon Henniger, Sibi Raja, Tarun Prasad, Chloe R Loughridge, Federico Cassano, Sabrina Ruixin Hu, Jianang Yang, William E. Byrd, Robert Zinkov, and Nada Amin. 2024. VerM-CTS: Synthesizing multi-step programs using a verifier, a large language model, and tree search. In The 4th… find
  3. Cameron B Browne, Edward Powley, Daniel Whitehouse, Simon M Lucas, Peter I Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton. 2012. A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in games, 4(1):1–43… find
  4. Guoxin Chen, Minpeng Liao, Chengxi Li, and Kai Fan. 2024. Alphamath almost zero: Process supervision without process. In The Thirty-eighth Annual Conference on Neural Information Processing Systems.
  5. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark… — Training Verifiers to Solve Math Word Problems
  6. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, and 1 others. 2021b. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168.
  7. Haonan Dong, Wenhao Zhu, Guojie Song, and Liang Wang. 2025. Aurora: Breaking low-rank bottleneck of lora with nonlinear mapping. arXiv preprint arXiv:2505.18738. — [arXiv:2505.18738](https://arxiv.org/abs/2505.18738) find
  8. Yangyi Fang, Jiaye Lin, Xiaoliang Fu, Cong Qin, Haolin Shi, Chaowen Hu, Lu Pan, Ke Zeng, and Xunliang Cai. 2026a. How to allocate, how to learn? dynamic rollout allocation and advantage modulation for policy optimization. arXiv preprint arXiv:2602.19208. find
  9. Yangyi Fang, Jiaye Lin, Xiaoliang Fu, Cong Qin, Haolin Shi, Chang Liu, and Peilin Zhao. 2026b. Proximity-based multi-turn optimization: Practical credit assignment for llm agent training. arXiv preprint arXiv:2602.19225. find
  10. Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Chaowen Hu, Cong Qin, Zekai Shao, Binbin Zheng, Lu Pan, and Ke Zeng. 2026a. From log π to π: Taming divergence in soft clipping via bilateral decoupled decay of probability gradient weight. arXiv preprint arXiv:2603.14389. — [arXiv:2603.14389](https://arxiv.org/abs/2603.14389) find
  11. Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Binbin Zheng, Chaowen Hu, Zekai Shao, Cong Qin, Lu Pan, Ke Zeng, and Xunliang Cai. 2026b. Maspo: Unifying gradient utilization, probability mass, and signal reliability for robust and sample-efficient llm reasoning. arXiv preprint arXiv:2602.17550.
  12. Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821.
  13. Kehan Jiang, Haonan Dong, Zhaolu Kang, Zhengzhou Zhu, and Guojie Song. 2026. Foe: Forest of errors makes the first solution the best in large reasoning models. arXiv preprint arXiv:2604.02967.
  14. Yiyang Jiang, Guangwu Qian, Jiaxin Wu, Qi Huang, Qing Li, Yongkang Wu, and Xiao-Yong Wei. 2025. Self-paced learning for images of antinuclear antibodies. IEEE Transactions on Medical Imaging. find
  15. Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021. Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies. Transactions of the Association for Computational Linguistics (TACL). find
  16. Yiyang Jiang, Wengyu Zhang, Xulu Zhang, Xiao-Yong Wei, Chang Wen Chen, and Qing Li. 2024. Prior knowledge integration via llm encoding and pseudo event regulation for video moment retrieval. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 7249–7258. find
  17. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783.
  18. Bo Li, Mingda Wang, Shikun Zhang, and Wei Ye. 2026a. Instruction data selection via answer divergence. Preprint, arXiv:2604.10448. find
  19. Etash Guha, Ryan Marten, Sedrick Keh, Negin Raoof, Georgios Smyrnis, Hritik Bansal, Marianna Nezhurina, Jean Mercat, Trung Vu, Zayne Sprague, and 1 others. 2025. Openthoughts: Data recipes for reasoning models. arXiv preprint arXiv:2506.04178.
  20. Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, and Maosong Sun. 2024. Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems. pag…
  21. Bo Li, Shikun Zhang, and Wei Ye. 2026b. Data selection for multi-turn dialogue instruction tuning. Preprint, arXiv:2604.07892. find
  22. Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2022. Contrastive decoding: Open-ended text generation as optimization. arXiv preprint arXiv:2210.15097.
  23. Yanshu Li, JianJiang Yang, Bozheng Li, and Ruixiang Tang. 2025a. Cama: Enhancing multimodal in-context learning with context-aware modulated attention. arXiv e-prints, pages arXiv–2505. find
  24. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. Measuring mathematical problem solving with the MATH dataset. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
  25. Yanshu Li, Jianjiang Yang, Tian Yun, Pinyuan Feng, Jinfa Huang, and Ruixiang Tang. 2025b. Taco: Enhancing multimodal in-context learning via task mapping-guided sequence configuration. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 736–763.
  26. Wentao Hu, Wengyu Zhang, Yiyang Jiang, Chen Jason Zhang, Xiaoyong Wei, and Li Qing. 2025. Removal of hallucination on hallucination: Debate-augmented rag. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15839–15853.
  27. Zixu Li, Yupeng Hu, Zhiwei Chen, Qinlei Huang, Guozhi Qiu, Zhiheng Fu, and Meng Liu. 2026c. Retrack: Evidence-driven dual-stream directional anchor calibration network for composed video retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 23373–23381. find
  28. Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Kai Dang, and 1 others. 2024. Qwen2. 5-coder technical report. arXiv preprint arXiv:2409.12186. — Qwen2.5-Coder Technical Report
  29. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix,… find
  30. Zixu Li, Yupeng Hu, Zhiwei Chen, Shiqi Zhang, Qinlei Huang, Zhiheng Fu, and Yinwei Wei. 2026d. Habit: Chrono-synergia robust progressive learning framework for composed image retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 6762–6770. find
  31. Jiaye Lin, Yifu Guo, Yuzhen Han, Sen Hu, Ziyi Ni, Licheng Wang, Mingguang Chen, Hongzhang Liu, Ronghao Chen, Yangfan He, and 1 others. 2025. Se-agent: Self-evolution trajectory optimization in multi-step reasoning with llm-based agents. arXiv preprint arXiv:2508.02085. — [arXiv:2508.02085](https://arxiv.org/abs/2508.02085) find
  32. Haoxiong Liu, Yifan Zhang, Yifan Luo, and Andrew C Yao. 2025a. Augmenting math word problems via iterative question composing. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 24605–24613.
  33. Michael Luo, Sijun Tan, Justin Wong, Xiaoxiang Shi, William Y Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Tianjun Zhang, Li Erran Li, and 1 others. 2025. Deepscaler: Surpassing o1-preview with a 1.5 b model by scaling rl. Notion Blog, 3(5). find
  34. Peiyang Liu. 2024. Unsupervised corrupt data detection for text training. Expert Systems with Applications, 248:123335. find
  35. Kexin Ma, Ruochun Jin, Wang Haotian, Wang Xi, Huan Chen, Yuhua Tang, and Qian Wang. 2024. Context-driven index trimming: A data quality perspective to enhancing precision of ralms. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 4886–4901. find
  36. Peiyang Liu, Ziqiang Cui, Di Liang, and Wei Ye. 2025b. Who stole your data? a method for detecting unauthorized rag theft. arXiv preprint arXiv:2510.07728. — [arXiv:2510.07728](https://arxiv.org/abs/2510.07728) find
  37. Peiyang Liu, Sen Wang, Xi Wang, Wei Ye, and Shikun Zhang. 2021a. Quadrupletbert: An efficient model for embedding-based large-scale retrieval. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3734… find
  38. Peiyang Liu, Xi Wang, Ziqiang Cui, and Wei Ye. 2025c. Queries are not alone: Clustering text embeddings for video search. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 874– 883. find
  39. Peiyang Liu, Xi Wang, Lin Wang, Wei Ye, Xiangyu Xi, and Shikun Zhang. 2021b. Distilling knowledge from bert into simple fully connected neural networks for efficient vertical retrieval. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pages 3965–3975. find
  40. Peiyang Liu, Xi Wang, Sen Wang, Wei Ye, Xiangyu Xi, and Shikun Zhang. 2021c. Improving embedding-based large-scale retrieval via label enhancement. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 133–142. find
  41. Peiyang Liu, Xiangyu Xi, Wei Ye, and Shikun Zhang. 2022. Label smoothing for text mining. In Proceedings of the 29th international conference on computational linguistics, pages 2210–2219. find
  42. Peiyang Liu, Jinyu Yang, Lin Wang, Sen Wang, Yunlai Hao, and Huihui Bai. 2023. Retrieval-based unsupervised noisy label detection on text data. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pages 4099–4104. find
  43. Kexin Ma, Bojun Li, Yuhua Tang, Liting Sun, and Ruochun Jin. 2026a. Cast: Character-and-scene episodic memory for agents. arXiv preprint arXiv:2602.06051. — [arXiv:2602.06051](https://arxiv.org/abs/2602.06051) find
  44. Xiaoxu Ma, Runhao Li, Hanwen Liu, Xiangbo Zhang, and Zhenyu Weng. 2026b. Unihash: Unifying pointwise and pairwise hashing paradigms for seen and unseen category retrieval. arXiv preprint arXiv:2601.09828. — [arXiv:2601.09828](https://arxiv.org/abs/2601.09828) find
  45. Xiaoxu Ma, Xiangbo Zhang, and Zhenyu Weng. 2026c. Stable and explainable personality trait evaluation in large language models with internal activations. arXiv preprint arXiv:2601.09833.
  46. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. 2023. Self-refine: Iterative refinement with… find
  47. Aaron Meurer, Christopher P Smith, Mateusz Paprocki, Ondřej Čertík, Sergey B Kirpichev, Matthew Rocklin, AMiT Kumar, Sergiu Ivanov, Jason K Moore, Sartaj Singh, and 1 others. 2017. Sympy: symbolic computing in python. PeerJ Computer Science, 3:e103. find
  48. Qizhi Pei, Lijun Wu, Zhuoshi Pan, Yu Li, Honglin Lin, Chenlin Ming, Xin Gao, Conghui He, and Rui Yan. 2025. Mathfusion: Enhancing mathematic problem-solving of llm through instruction fusion. arXiv preprint arXiv:2503.16212. — [arXiv:2503.16212](https://arxiv.org/abs/2503.16212) find
  49. Peiyang Liu, Wei Ye, Xiangyu Xi, Tong Wang, Jinglei Zhang, and Shikun Zhang. 2020. Not all synonyms are created equal: Incorporating similarity of synonyms to enhance word embeddings. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE. find
  50. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023. Direct preference optimization: Your language model is secretly a reward model. Advances in neural information processing systems, 36:53728–53741.
  51. Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Jian-guang Lou, Chongyang Tao, Xiubo Geng, Qingwei Lin, Shifeng Chen, and Dongmei Zhang. 2023. Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct. arXiv preprint arXiv:2308.09583.
  52. Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020. Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pages 3505–3506. find
  53. Yanwei Ren, Haotian Zhang, Fuxiang Wu, Jiayan Qiu, Jiaxing Huang, Baosheng Yu, and Liu Liu. 2025. Sigma: Refining large language model reasoning via sibling-guided monte carlo augmentation. arXiv preprint arXiv:2506.06470. — [arXiv:2506.06470](https://arxiv.org/abs/2506.06470) find
  54. David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli. 2019. Analysing mathematical reasoning abilities of neural models. In International Conference on Learning Representations.
  55. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, and 1 others. 2024. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300.
  56. Zhengyang Tang, Xingxing Zhang, Benyou Wang, and Furu Wei. 2024. Mathscale: Scaling instruction tuning for mathematical reasoning. In Forty-first International Conference on Machine Learning. find
  57. Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma, Chenhao Li, and Luan Zhang. 2025a. Compkbqa: Component-wise task decomposition for knowledge base question answering. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processi… find
  58. Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Hao Wang, Jun Yang, Jing Xu, Ruanmin Cao, and Haoyu Wang. 2024. Augmenting reasoning capabilities of llms with graph structures in knowledge base question answering. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 1… find
  59. Yuhang Tian, Pan Yang, Dandan Song, Zhijing Wu, and Hao Wang. 2025b. Grv-kbqa: A three-stage framework for knowledge base question answering with decoupled logical structure, semantic grounding and structure-aware validation. In Findings of the Association for Computational Linguistics: EMNLP 2025,… find
  60. Yuxuan Tong, Xiwen Zhang, Rui Wang, Ruidong Wu, and Junxian He. 2024. DART-math: Difficulty-aware rejection tuning for mathematical problem-solving. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. find
  61. Tom Vodopivec and Branko Šter. 2014. Enhancing upper confidence bounds for trees with temporal difference values. In 2014 IEEE conference on computational intelligence and games, pages 1–8. IEEE. find
  62. Peiyi Wang, Lei Li, Zhihong Shao, Runxin Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, and Zhifang Sui. 2024. Math-shepherd: Verify and reinforce llms step-by-step without human annotations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Paper…
  63. Shansong Wang, Mingzhe Hu, Qiang Li, Mojtaba Safari, and Xiaofeng Yang. 2025. Capabilities of gpt-5 on multimodal medical reasoning. arXiv preprint arXiv:2508.08224. — [arXiv:2508.08224](https://arxiv.org/abs/2508.08224) find
  64. Zi-Han Wang, Lam Nguyen, Zhengyang Zhao, Mengyue Yang, Chengwei Qin, Yujiu Yang, and Linyi Yang. 2026. Creativebench: Benchmarking and enhancing machine creativity via self-evolving challenges. arXiv preprint arXiv:2603.11863.
  65. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems, volume 35, pages 24824–24837. Curran Associates, Inc.
  66. Junhao Xiao, Zhiyu Wu, Hao Lin, Yi Chen, Yahui Liu, Xiaoran Zhao, Zixu Wang, and Zejiang He. 2026. Not just what’s there: Enabling clip to comprehend negated visual descriptions without fine-tuning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 10978–10986. find
  67. Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, and Young Jin Kim. 2024a. Contrastive preference optimization: Pushing the boundaries of llm performance in machine translation. arXiv preprint arXiv:2401.08417.
  68. Hongshen Xu, Zihan Wang, Zichen Zhu, Lei Pan, Xingyu Chen, Shuai Fan, Lu Chen, and Kai Yu. 2025. Alignment for efficient tool calling of large language models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 17787–17803.
  69. Hongshen Xu, Zichen Zhu, Lei Pan, Zihan Wang, Su Zhu, Da Ma, Ruisheng Cao, Lu Chen, and Kai Yu. 2024b. Reducing tool hallucination via reliability alignment. arXiv preprint arXiv:2412.04141. — [arXiv:2412.04141](https://arxiv.org/abs/2412.04141) find
  70. Yuchen Yan, Peiyan Zhang, Zheng Fang, and Qingqing Long. 2024. Inductive graph alignment prompt: bridging the gap between graph pre-training and inductive fine-tuning from spectral perspective. In Proceedings of the ACM Web Conference 2024, pages 4328–4339. find
  71. An Yang, Beichen Zhang, Binyuan Hui, Bofei Gao, Bowen Yu, Chengpeng Li, Dayiheng Liu, Jianhong Tu, Jingren Zhou, Junyang Lin, and 1 others. 2024. Qwen2. 5-math technical report: Toward mathematical expert model via self-improvement. arXiv preprint arXiv:2409.12122. — Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
  72. Limei Yao, Kexin Ma, Ruochun Jin, Haoqi Zheng, and Dong Wang. 2025. Enhancing vector data quality through negative learning for retrieval-augmented large models. In 2025 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE. find
  73. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik R Narasimhan. 2023. Tree of thoughts: Deliberate problem solving with large language models. In Thirty-seventh Conference on Neural Information Processing Systems. find
  74. Zheng Yuan, Hongyi Yuan, Chengpeng Li, Guanting Dong, Keming Lu, Chuanqi Tan, Chang Zhou, and Jingren Zhou. 2023. Scaling relationship on learning mathematical reasoning with large language models. arXiv preprint arXiv:2308.01825.
  75. Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Pan Lu, Zhi Huang, Carlos Guestrin, and James Zou. 2025. Optimizing generative ai by backpropagating language model feedback. Nature, 639:609–616. find
  76. Wei Zhu, Zhiwen Tang, and Kun Yue. 2026a. Symphony: Synergistic multi-agent planning with heterogeneous language model assembly. arXiv preprint arXiv:2601.22623. — [arXiv:2601.22623](https://arxiv.org/abs/2601.22623) find
  77. Wei Zhu, Lixing Yu, Hao-Ren Yao, Zhiwen Tang, and Kun Yue. 2026b. Task-aware llm council with adaptive decision pathways for decision support. arXiv preprint arXiv:2601.22662. — [arXiv:2601.22662](https://arxiv.org/abs/2601.22662) find
  78. Geigh Zollicoffer, Tanush Chopra, Mingkuan Yan, Xiaoxu Ma, Kenneth Eaton, and Mark Riedl. 2025. World model robustness via surprise recognition. arXiv preprint arXiv:2512.01119. — [arXiv:2512.01119](https://arxiv.org/abs/2512.01119) find
  79. Dan Zhang, Sining Zhoubian, Ziniu Hu, Yisong Yue, Yuxiao Dong, and Jie Tang. 2024a. Rest-mcts*: Llm self-training via process reward guided tree search. Advances in Neural Information Processing Systems, 37:64735–64772.
  80. Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, and Sunghun Kim. 2023. Continual learning on dynamic graphs via parameter isolation. In Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval, pages 601–611. find
  81. Peiyan Zhang, Yuchen Yan, Xi Zhang, Chaozhuo Li, Senzhang Wang, Feiran Huang, and Sunghun Kim. 2024b. Transgnn: Harnessing the collaborative power of transformers and graph neural networks for recommender systems. In Proceedings of the 47th International ACM SIGIR conference on research and developm… find
  82. Qianchi Zhang, Hainan Zhang, Liang Pang, Yongxin Tong, Hongwei Zheng, and Zhiming Zheng. 2025a. Less is more: Compact clue selection for efficient retrieval-augmented generation reasoning. arXiv preprint arXiv:2502.11811. — [arXiv:2502.11811](https://arxiv.org/abs/2502.11811) find
  83. Wenyuan Zhang, Tianyun Liu, Mengxiao Song, Xiaodong Li, and Tingwen Liu. 2025b. Sotopia-: Dynamic strategy injection learning and social instruction following evaluation for social agents. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pape… find
  84. Wenyuan Zhang, Shuaiyi Nie, Xinghua Zhang, Zefeng Zhang, and Tingwen Liu. 2025c. S1-bench: A simple benchmark for evaluating system 1 thinking capability of large reasoning models. arXiv preprint arXiv:2504.10368. — [arXiv:2504.10368](https://arxiv.org/abs/2504.10368) find
  85. Yuanjun Zhang, Fuzel Ahamed Shaik, Suvojit Acharjee, Fahad Khalid, and Mourad Oussalah. 2026. Towards reliable multimodal disaster severity assessment through preference optimization and explainable vision-language reasoning. Reliability Engineering & System Safety, page 112674. find

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