---
title: "Who cites Michal Valko — 560 papers of our library"
description: "560 papers of the Elamur Research library cite the works of Michal Valko, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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---
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# Who cites Michal Valko

581 citations of their works from 560 papers of our library (of the 149,439 whose bibliographies we read) · 104 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Michal Valko](https://md.research.elamur.ai/free/en/authors/michal-valko/)

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1. [**Learning Transferable Visual Models From Natural Language Supervision arXiv:2103.00020**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,767 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
2. [**The Llama 3 Herd of Models arXiv:2407.21783**Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.*2024 · 7,682 citations*](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
3. **[Emerging Properties in Self-Supervised Vision Transformers](https://md.research.elamur.ai/free/en/papers/caron-2021-emerging-properties-self-supervised-vision/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Mathilde Caron, Hugo Touvron, Ishan Misra, et al.*2021 · 6,046 citations*
4. **[HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://md.research.elamur.ai/free/en/papers/hsu-2021-hubert-self-supervised-speech-representation/) [IEEE/ACM Transactions on Audio Speech and Language Processing](https://md.research.elamur.ai/free/en/venues/ieee-acm-transactions-on-audio-speech-and-language-processing/)**Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, et al.*2021 · 3,458 citations*
5. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
6. **[Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://md.research.elamur.ai/free/en/papers/jia-2021-scaling-up-visual-vision-language/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Chao Jia, Yinfei Yang, Ye Xia, et al.*2021 · 1,615 citations*
7. [**DINOv2: Learning Robust Visual Features without Supervision arXiv:2304.07193**Maxime Oquab, Timothee Darcet, Theo Moutakanni, et al.*2023 · 1,513 citations*](https://md.research.elamur.ai/free/en/papers/oquab-2023-dinov2-learning-robust-visual-features/)
8. [**Masked Autoencoders Are Scalable Vision Learners arXiv:2111.06377**Kaiming He, Xinlei Chen, Saining Xie, et al.*2021 · 596 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-masked-autoencoders-scalable-vision-learners/)
9. [**CogView: Mastering Text-to-Image Generation via Transformers arXiv:2105.13290**Ming Ding, Zhuoyi Yang, Wenyi Hong, et al.*2021 · 469 citations*](https://md.research.elamur.ai/free/en/papers/ding-2021-cogview-mastering-text-image/)
10. [**OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework arXiv:2202.03052**Peng Wang, An Yang, Rui Men, et al.*2022 · 467 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-ofa-unifying-architectures-tasks-modalities/)
11. **[DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings](https://md.research.elamur.ai/free/en/papers/chuang-2022-diffcse-difference-based-contrastive-learning/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, et al.*2022 · 269 citations*
12. **[Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/wang-2024-interpretable-preferences-multi-objective-reward/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haoxiang Wang, Wei Xiong, Tengyang Xie, et al.*2024 · 153 citations*
13. [**Process Reinforcement through Implicit Rewards arXiv:2502.01456**Ganqu Cui, Lifan Yuan, Zefan Wang, et al.*2025 · 151 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-process-reinforcement-through-implicit-rewards/)
14. [**Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing arXiv:2406.08464**Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al.*2024 · 148 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-magpie-alignment-data-synthesis-scratch/)
15. [**InternVideo: General Video Foundation Models via Generative and Discriminative Learning arXiv:2212.03191**Yi Wang, Kunchang Li, Yizhuo Li, et al.*2022 · 144 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-internvideo-general-video-foundation/)
16. **[ReFT: Reasoning with Reinforced Fine-Tuning](https://md.research.elamur.ai/free/en/papers/trung-2024-reft-reasoning-reinforced-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luong Trung, Xinbo Zhang, Zhanming Jie, et al.*2024 · 134 citations*
17. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
18. [**Fanar: An Arabic-Centric Multimodal Generative AI Platform arXiv:2501.13944**Fanar Team: Ummar Abbas, Mohammad Shahmeer Ahmad, Firoj Alam, et al.*2025 · 127 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2025-fanar-arabic-centric-multimodal-generative/)
19. [**SaulLM-7B: A pioneering Large Language Model for Law arXiv:2403.03883**Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/colombo-2024-saullm-7b-pioneering-large-language/)
20. [**ALLaM: Large Language Models for Arabic and English arXiv:2407.15390**M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/bari-2024-allam-large-language-models-arabic/)
21. [**Orca-Math: Unlocking the potential of SLMs in Grade School Math arXiv:2402.14830**Arindam Mitra, Hamed Khanpour, Corby Rosset, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2024-orca-math-unlocking-potential-slms/)
22. [**RLHF Workflow: From Reward Modeling to Online RLHF arXiv:2405.07863**Hanze Dong, Wei Xiong, Bo Pang, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-rlhf-workflow-reward-modeling-online/)
23. [**Nemotron-4 340B Technical Report arXiv:2406.11704**Nvidia: Bo Adler, Niket Agarwal, Ashwath Aithal, et al.*2024 · 107 citations*](https://md.research.elamur.ai/free/en/papers/adler-2024-nemotron-4-340b-technical-report/)
24. **[Multi-Step Reasoning with Large Language Models, a Survey](https://md.research.elamur.ai/free/en/papers/plaat-2024-multi-step-reasoning-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Aske Plaat, Annie Wong, Suzan Verberne, et al.*2024 · 106 citations*
25. [**AnglE-optimized Text Embeddings arXiv:2309.12871**Xianming Li, Jing Li*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-angle-optimized-text-embeddings/)
26. [**Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint arXiv:2312.11456**Wei Xiong, Hanze Dong, Chenlu Ye, et al.*2023 · 100 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2023-iterative-preference-learning-human-feedback/)
27. **[A Survey of Reinforcement Learning from Human Feedback](https://md.research.elamur.ai/free/en/papers/kaufmann-2023-survey-reinforcement-learning-human-feedback/) [Transactions on Machine Learning Research](https://md.research.elamur.ai/free/en/venues/transactions-on-machine-learning-research/)**Timo Kaufmann, Paul Weng, Viktor Bengs, et al.*2023 · 92 citations*
28. **[Mitigating the Alignment Tax of RLHF](https://md.research.elamur.ai/free/en/papers/lin-2024-mitigating-alignment-tax-rlhf/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yong Lin, Hangyu Lin, Wei Xiong, et al.*2024 · 83 citations*
29. [**Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive arXiv:2402.13228**Arka Pal, Deep Karkhanis, Samuel Dooley, et al.*2024 · 82 citations*](https://md.research.elamur.ai/free/en/papers/pal-2024-smaug-fixing-failure-modes-preference/)
30. [**Reinforcement Learning via Self-Distillation arXiv:2601.20802**Jonas Hubotter, Frederike Lubeck, Lejs Behric, et al.*2026 · 74 citations*](https://md.research.elamur.ai/free/en/papers/hubotter-2026-reinforcement-learning-self-distillation/)
31. [**From r to Q*: Your Language Model is Secretly a Q-Function arXiv:2404.12358**Rafael Rafailov, Joey Hejna, Ryan Park, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/rafailov-2024-r-q-language-model-secretly/)
32. **[Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards](https://md.research.elamur.ai/free/en/papers/wang-2024-arithmetic-control-llms-diverse-user/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxiang Wang, Yong Lin, Wei Xiong, et al.*2024 · 68 citations*
33. **[Towards Understanding Grokking: An Effective Theory of Representation Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-towards-understanding-grokking-effective-theory/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ziming Liu, Ouail Kitouni, Niklas Nolte, et al.*2022 · 65 citations*
34. **[The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism](https://md.research.elamur.ai/free/en/papers/song-2025-good-bad-greedy-evaluation-llms/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yifan Song, Guoyin Wang, Sujian Li, et al.*2025 · 64 citations*
35. [**Free Process Rewards without Process Labels arXiv:2412.01981**Lifan Yuan, Wendi Li, Huayu Chen, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-free-process-rewards-process-labels/)
36. [**Generative Reward Models arXiv:2410.12832**Dakota Mahan, Duy Van Phung, Rafael Rafailov, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/mahan-2024-generative-reward-models/)
37. **[RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold](https://md.research.elamur.ai/free/en/papers/setlur-2024-rl-incorrect-synthetic-data-scales/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Amrith Setlur, Saurabh Garg, Xinyang Geng, et al.*2024 · 56 citations*
38. [**Pedagogical Alignment of Large Language Models Findings of the Association for Computational Linguistics: EMNLP 2024**Shashank Sonkar, Kangqi Ni, Sapana Chaudhary, et al.*2024 · 54 citations*](https://md.research.elamur.ai/free/en/papers/sonkar-2024-pedagogical-alignment-large-language-models/)
39. [**Critique-out-Loud Reward Models arXiv:2408.11791**Zachary Ankner, Mansheej Paul, Brandon Cui, et al.*2024 · 52 citations*](https://md.research.elamur.ai/free/en/papers/ankner-2024-critique-out-loud-reward-models/)
40. **[Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision](https://md.research.elamur.ai/free/en/papers/sun-2024-easy-hard-generalization-scalable-alignment/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhiqing Sun, Longhui Yu, Yikang Shen, et al.*2024 · 51 citations*
41. **[mDPO: Conditional Preference Optimization for Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-mdpo-conditional-preference-optimization-multimodal/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fei Wang, Wenxuan Zhou, James Y. Huang, et al.*2024 · 50 citations*
42. **[Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback](https://md.research.elamur.ai/free/en/papers/ivison-2024-unpacking-dpo-ppo-disentangling-best/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hamish Ivison, Yizhong Wang, Jiacheng Liu, et al.*2024 · 50 citations*
43. [**Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences arXiv:2404.03715**Corby Rosset, Ching-An Cheng, Arindam Mitra, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/rosset-2024-direct-nash-optimization-teaching-language/)
44. [**Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization arXiv:2411.10442**Weiyun Wang, Zhe Chen, Wenhai Wang, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-enhancing-reasoning-ability-multimodal-large/)
45. **[AI-Researcher: Autonomous Scientific Innovation](https://md.research.elamur.ai/free/en/papers/tang-2025-ai-researcher-autonomous-scientific/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jiabin Tang, Lianghao Xia, Zhonghang Li, et al.*2025 · 40 citations*
46. [**A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems arXiv:2504.09037**Zixuan Ke, Fangkai Jiao, Yifei Ming, et al.*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/ke-2025-survey-frontiers-llm-reasoning-inference/)
47. [**ODIN: Disentangled Reward Mitigates Hacking in RLHF arXiv:2402.07319**Lichang Chen, Chen Zhu, Davit Soselia, et al.*2024 · 38 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-odin-disentangled-reward-mitigates-hacking/)
48. [**Data Distillation: A Survey arXiv:2301.04272**Noveen Sachdeva, Julian McAuley*2023 · 37 citations*](https://md.research.elamur.ai/free/en/papers/sachdeva-2023-data-distillation-survey/)
49. **[Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models](https://md.research.elamur.ai/free/en/papers/ranaldi-2024-self-refine-instruction-tuning-aligning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Leonardo Ranaldi, Andre Freitas*2024 · 35 citations*
50. [**Nash Learning from Human Feedback arXiv:2312.00886**Remi Munos, Michal Valko, Daniele Calandriello, et al.*2023 · 32 citations*](https://md.research.elamur.ai/free/en/papers/munos-2023-nash-learning-human-feedback/)
51. **[LiPO: Listwise Preference Optimization through Learning-to-Rank](https://md.research.elamur.ai/free/en/papers/liu-2025-lipo-listwise-preference-optimization-through/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Tianqi Liu, Zhen Qin, Junru Wu, et al.*2025 · 31 citations*
52. **[Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing](https://md.research.elamur.ai/free/en/papers/jiao-2024-learning-planning-based-reasoning-trajectories/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fangkai Jiao, Chengwei Qin, Zhengyuan Liu, et al.*2024 · 31 citations*
53. [**LLM-as-a-Judge & Reward Model: What They Can and Cannot Do arXiv:2409.11239**Guijin Son, Hyunwoo Ko, Hoyoung Lee, et al.*2024 · 27 citations*](https://md.research.elamur.ai/free/en/papers/son-2024-llm-judge-reward-model-they/)
54. **[Binary Classifier Optimization for Large Language Model Alignment](https://md.research.elamur.ai/free/en/papers/jung-2025-binary-classifier-optimization-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Seungjae Jung, Gunsoo Han, Daniel Wontae Nam, et al.*2025 · 26 citations*
55. **[NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions](https://md.research.elamur.ai/free/en/papers/yuan-2025-naturalreasoning-reasoning-wild-2-8m/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Weizhe Yuan, Jane Yu, Song Jiang, et al.*2025 · 26 citations*
56. [**BOND: Aligning LLMs with Best-of-N Distillation arXiv:2407.14622**Pier Giuseppe Sessa, Robert Dadashi, Leonard Hussenot, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/sessa-2024-bond-aligning-llms-best-n/)
57. [**Step-Controlled DPO: Leveraging Stepwise Error for Enhanced Mathematical Reasoning arXiv:2407.00782**Zimu Lu, Aojun Zhou, Ke Wang, et al.*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-step-controlled-dpo-leveraging-stepwise/)
58. [**Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation arXiv:2410.02725**Rohin Manvi, Anikait Singh, Stefano Ermon*2024 · 22 citations*](https://md.research.elamur.ai/free/en/papers/manvi-2024-adaptive-inference-time-compute-llms/)
59. [**Personality Alignment of Large Language Models arXiv:2408.11779**Minjun Zhu, Yixuan Weng, Linyi Yang, et al.*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-personality-alignment-large-language-models/)
60. **[Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment](https://md.research.elamur.ai/free/en/papers/oosterlinck-2025-anchored-preference-optimization-contrastive-revisions/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Karel D'Oosterlinck, Winnie Xu, Chris Develder, et al.*2025 · 20 citations*
61. [**UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models arXiv:2502.00334**Xin Xu, Qiyun Xu, Tong Xiao, et al.*2025 · 19 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-ugphysics-comprehensive-benchmark-undergraduate-physics/)
62. **[Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2024-weak-strong-search-align-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhanhui Zhou, Zhixuan Liu, Jie Liu, et al.*2024 · 17 citations*
63. **[Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels](https://md.research.elamur.ai/free/en/papers/franken-2024-self-supervised-alignment-mutual-information/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jan-Philipp Franken, Eric Zelikman, Rafael Rafailov, et al.*2024 · 16 citations*
64. [**Outcome-based Exploration for LLM Reasoning arXiv:2509.06941**Yuda Song, Julia Kempe, Remi Munos*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/song-2025-outcome-based-exploration-llm-reasoning/)
65. **[MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teaming](https://md.research.elamur.ai/free/en/papers/guo-2025-mtsa-multi-turn-safety-alignment/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weiyang Guo, Jing Li, Wenya Wang, et al.*2025 · 14 citations*
66. **[Discovering Preference Optimization Algorithms with and for Large Language Models](https://md.research.elamur.ai/free/en/papers/lu-2024-discovering-preference-optimization-algorithms-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Chris Lu, Samuel Holt, Claudio Fanconi, et al.*2024 · 14 citations*
67. [**The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization arXiv:2403.17031**Shengyi Huang, Michael Noukhovitch, Arian Hosseini, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-n-implementation-details-rlhf-ppo/)
68. **[Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/sui-2026-meta-reasoner-dynamic-guidance-optimized/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuan Sui, Yufei He, Tri Cao, et al.*2026 · 13 citations*
69. [**SaRO: Enhancing LLM Safety through Reasoning-based Alignment arXiv:2504.09420**Yutao Mou, Yuxiao Luo, Shikun Zhang, et al.*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/mou-2025-saro-enhancing-llm-safety-through/)
70. **[Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning](https://md.research.elamur.ai/free/en/papers/akter-2026-nemotron-crossthink-scaling-self-learning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Syeda Nahida Akter, Shrimai Prabhumoye, Matvei Novikov, et al.*2026 · 9 citations*
71. [**Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering arXiv:2411.11504**Xinyan Guan, Yanjiang Liu, Xinyu Lu, et al.*2024 · 9 citations*](https://md.research.elamur.ai/free/en/papers/guan-2024-search-verify-feedback-towards-next/)
72. **[Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2025-genius-generalizable-purely-unsupervised-self/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fangzhi Xu, Hang Yan, Chang Ma, et al.*2025 · 8 citations*
73. **[Selective Preference Optimization via Token-Level Reward Function Estimation](https://md.research.elamur.ai/free/en/papers/yang-2025-selective-preference-optimization-token-level/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kailai Yang, Zhiwei Liu, Qianqian Xie, et al.*2025 · 8 citations*
74. **[MPO: Multilingual Safety Alignment via Reward Gap Optimization](https://md.research.elamur.ai/free/en/papers/zhao-2025-mpo-multilingual-safety-alignment-reward/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weixiang Zhao, Yulin Hu, Yang Deng, et al.*2025 · 7 citations*
75. **[MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs](https://md.research.elamur.ai/free/en/papers/liu-2025-metafaith-faithful-natural-language-uncertainty/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Gabrielle Kaili-May Liu, Gal Yona, Avi Caciularu, et al.*2025 · 6 citations*
76. **[FanarGuard: A Culturally-Aware Moderation Filter for Arabic Language Models](https://md.research.elamur.ai/free/en/papers/fatehkia-2026-fanarguard-culturally-aware-moderation-filter/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Masoomali Fatehkia, Enes Altinisik, Husrev Taha Sencar*2026 · 5 citations*
77. **[GAM: Hierarchical Graph-based Agentic Memory for LLM Agents](https://md.research.elamur.ai/free/en/papers/wu-2026-gam-hierarchical-graph-based-agentic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhaofen Wu, Hanrong Zhang, Fulin Lin, et al.*2026 · 5 citations*
78. **[MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training](https://md.research.elamur.ai/free/en/papers/huang-2025-musc-improving-complex-instruction-following/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hui Huang, Jiaheng Liu, Yancheng He, et al.*2025 · 4 citations*
79. **[MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills](https://md.research.elamur.ai/free/en/papers/yao-2026-medqa-cs-objective-structured-clinical/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zonghai Yao, Zihao Zhang, Chaolong Tang, et al.*2026 · 3 citations*
80. **[Bias Fitting to Mitigate Length Bias of Reward Model in RLHF](https://md.research.elamur.ai/free/en/papers/zhao-2026-bias-fitting-mitigate-length-bias/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kangwen Zhao, Jianfeng Cai, Jinhua Zhu, et al.*2026 · 2 citations*
81. **[Gained in Translation: Privileged Pairwise Judges Enhance Multilingual Reasoning](https://md.research.elamur.ai/free/en/papers/sutawika-2026-gained-translation-privileged-pairwise-judges/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lintang Sutawika, Gokul Swamy, Steven Wu, et al.*2026 · 2 citations*
82. **[Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM Alignment](https://md.research.elamur.ai/free/en/papers/tan-2026-beyond-compromise-pareto-lenient-consensus/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Renxuan Tan, Rongpeng Li, Zhifeng Zhao, et al.*2026 · 1 citation*
83. **[CoAct: Co-Active LLM Preference Learning with Human-AI Synergy](https://md.research.elamur.ai/free/en/papers/xu-2026-coact-co-active-llm-preference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruiyao Xu, Mihir Parmar, Tiankai Yang, et al.*2026 · 1 citation*
84. **[Energy Matching based Preference Learning for Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/shankar-2026-energy-matching-based-preference-learning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Shiv Shankar*2026 · 1 citation*
85. **[MEAV: Model Editing with Alignment Vectors for inference time LLM alignment in single and multidomain preference spectrum](https://md.research.elamur.ai/free/en/papers/shahriar-2026-meav-model-editing-alignment-vectors/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sadat Shahriar, Zheng Qi, Nikolaos Pappas, et al.*2026 · 1 citation*
86. **[What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context](https://md.research.elamur.ai/free/en/papers/ouyang-2026-what-makes-llms-effective-sequential/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhongyu Ouyang, Qianlong Wen, Chunhui Zhang, et al.*2026 · 1 citation*
87. [**Safe at the Margins: A General Approach to Safety Alignment in Low-Resource English Languages — A Singlish Case Study arXiv:2502.12485**Isaac Lim, Shaun Khoo, Roy Ka-Wei Lee, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/lim-2025-safe-margins-general-approach-safety/)
88. **[Alignment Tuning for Large Language Models: A Data-Centric Lens on Alignment Data Pipelines](https://md.research.elamur.ai/free/en/papers/song-2026-alignment-tuning-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hwanjun Song*2026*
89. **[BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy Optimization](https://md.research.elamur.ai/free/en/papers/reddy-2026-biasgrpo-stabilizing-bias-mitigation-high/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Saket Reddy, Ke Yang, ChengXiang Zhai*2026*
90. **[Context-Value-Action Architecture for Value-Driven Large Language Model Agents](https://md.research.elamur.ai/free/en/papers/zhang-2026-context-value-action-architecture-value/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**TianZe Zhang, Sirui Sun, Yuhang Xie, et al.*2026*
91. **[DynamicFocalPO: Adaptive Focusing Strategy for Preference Optimization](https://md.research.elamur.ai/free/en/papers/zhou-2026-dynamicfocalpo-adaptive-focusing-strategy-preference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shu Zhou, Junan Chen, Rui Ling, et al.*2026*
92. **[Experience is the Teacher: Reusing Atomic Thoughts from LLMs to Improve Medical Dialogue](https://md.research.elamur.ai/free/en/papers/yu-2026-experience-is-teacher-reusing-atomic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Guangya Yu, Hui Luo, Qi Ye, et al.*2026*
93. **[FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner](https://md.research.elamur.ai/free/en/papers/wu-2026-flair-steering-llm-mathematical-problem/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Wu, Hongru Sun, Wanqing Li, et al.*2026*
94. [**Flout at Your Own Risk: LLMs Struggle with Pragmatic Cooperativity Under Epistemic Asymmetry arXiv:2607.11053**Hannah VanderHoeven, Abhijnan Nath, Nikhil Krishnaswamy*2026*](https://md.research.elamur.ai/free/en/papers/vanderhoeven-2026-flout-own-risk-llms-struggle/)
95. **[From Explicit to Implicit: A Theoretical Framework and Transfer Method for Preference Internalization in Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-explicit-implicit-theoretical-framework-transfer/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Binrui Wang, Yongping Du, Yu Pei, et al.*2026*
96. **[Joint Optimization of Training Data and Policy in RLHF](https://md.research.elamur.ai/free/en/papers/yu-2026-joint-optimization-training-data-policy/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuohao Yu, Jiali Zeng, Weizheng Gu, et al.*2026*
97. **[Leveraging Unannotated Sign Language Data via a Robust Data Augmentation Method for Contrastive Representation Learning](https://md.research.elamur.ai/free/en/papers/madjoukeng-2026-leveraging-unannotated-sign-language-data/) [LREC Workshop on the Representation and Processing of Sign Languages: Language in Motion](https://md.research.elamur.ai/free/en/venues/signlang/)**Ariel Basso Madjoukeng, Pierre Poitier, Belise Edith Kenmogne, et al.*2026*
98. **[Mitigating Cultural Bias in LLMs via Multi-Agent Cultural Debate](https://md.research.elamur.ai/free/en/papers/tan-2026-mitigating-cultural-bias-llms-multi/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qian Tan, Lei Jiang, Yuting Zeng, et al.*2026*
99. **[Pseudo-Likelihood Training for Reasoning Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/shankar-2026-pseudo-likelihood-training-reasoning-diffusion/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Shiv Shankar*2026*
100. **[Revisiting Self-Play Preference Optimization: On the Role of Prompt Difficulty](https://md.research.elamur.ai/free/en/papers/xiao-2026-revisiting-self-play-preference-optimization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yao Xiao, Jung-jae Kim, Roy Ka-Wei Lee, et al.*2026*

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