| Challenge: | Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models with human preferences, but often at the cost of reduced output diversity. |
| Approach: | They propose a framework that incorporates intrinsic rewards for novel states alongside traditional sparse extrinsic rewards to optimize both output diversity and alignment quality. |
| Outcome: | The proposed framework achieves significant gains in diversity on multiple diversity-oriented metrics while maintaining alignment with human preferences comparable to standard RLHF. |
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| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is a powerful paradigm for aligning language models with human values and preferences. |
| Approach: | They propose to use gaze-aware reward models and gaze-based distribution of sparse rewards to enhance RLHF. |
| Outcome: | The proposed models achieve faster convergence while maintaining or slightly improving performance, reducing computational requirements during policy training. |
RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution (2025.emnlp-main)
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| Challenge: | Experimental results demonstrate the superiority of our approach to aligning large language models with human preferences. |
| Approach: | They propose a method that evaluates and assigns specific credit to each token using an off-the-shelf reward model. |
| Outcome: | The proposed method evaluates and assigns specific credit to each token using an off-the-shelf reward model. |
trlX: A Framework for Large Scale Reinforcement Learning from Human Feedback (2023.emnlp-main)
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Alexander Havrilla, Maksym Zhuravinskyi, Duy Phung, Aman Tiwari, Jonathan Tow, Stella Biderman, Quentin Anthony, Louis Castricato
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Improving Discriminative Capability of Reward Models in RLHF Using Contrastive Learning (2024.emnlp-main)
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Lu Chen, Rui Zheng, Binghai Wang, Senjie Jin, Caishuang Huang, Junjie Ye, Zhihao Zhang, Yuhao Zhou, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang
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Towards Pareto-Efficient RLHF: Paying Attention to a Few High-Reward Samples with Reward Dropout (2024.findings-emnlp)
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| Challenge: | RLHF is a bi-objective problem that has the nature of a Pareto optimization . reward dropout is generalizable and most effective with non-pretrained target models . |
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Optimizing Language Models with Fair and Stable Reward Composition in Reinforcement Learning (2024.emnlp-main)
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| Challenge: | Recent research has developed algorithms for reinforcement learning from human feedback and AI-generated feedback. |
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RLHF Algorithms Ranked: An Extensive Evaluation Across Diverse Tasks, Rewards, and Hyperparameters (2025.emnlp-industry)
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Lucas Spangher, Rama Kumar Pasumarthi, Nick Masiewicki, William F. Arnold, Aditi Kaushal, Dale Johnson, Peter Grabowski, Eugene Ie
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Prototypical Reward Network for Data-Efficient Model Alignment (2024.acl-long)
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| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is a reward model that fine-tunes Large Language Models (LLMs) by utilizing Prototypical Networks. |
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CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment (2024.lrec-main)
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Feiteng Fang, Liang Zhu, Xi Feng, Jinchang Hou, Qixuan Zhao, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Large language models (LLMs) have attracted considerable attention from academic and industrial communities due to their outstanding performance in various natural language processing tasks. |
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Inverse Reinforcement Learning Meets Large Language Model Alignment (2025.acl-tutorials)
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| Challenge: | This tutorial will provide a comprehensive review of recent advances in LLM alignment . it will highlight the necessity of constructing neural reward models from human data . |
| Approach: | This tutorial will provide a comprehensive review of recent advances in LLM alignment through the lens of inverse reinforcement learning. |
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