ClaHF: A Human Feedback-inspired Reinforcement Learning Framework for Improving Classification Tasks
Text classification models are typically trained via supervised fine-tuning (SFT). However, SFT essentially performs behavior cloning from instance-wise labels and thus fails to adequately capture relative preference relations among samples, which limits the model's ability to shape decision boundaries and calibrate predictive confidence. In this paper, we propose ClaHF, a human feedback-inspired reinforcement learning (RL) framework for text classification that integrates preference modeling and RL optimization into the classification pipeline without requiring additional human annotations. Unlike prior work that relies solely on instance-wise supervision, ClaHF constructs multiple candi...
| Authors | Tianxiang Xu, Xiaoyan Zhu, Xin Lai et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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