Papers by Karim Galliamov
Enhancing RLHF with Human Gaze Modeling (2025.emnlp-main)
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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. |