Papers with easy-to-learn
OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)
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Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu
| Challenge: | Existing RLHF frameworks face inference bottlenecks and complexity barriers restricting their accessibility for newcomers. |
| Approach: | They propose an open-source RLHF framework that can be used to train large language models. |
| Outcome: | The proposed framework achieves superior training efficiency with speedups ranging from 1.22 to 1.68 across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation. |
A Data Cartography based MixUp for Pre-trained Language Models (2022.naacl-main)
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| Challenge: | Using MixUp, additional samples are generated during training by combining random pairs of training samples and their labels. |
| Approach: | They propose a new MixUp strategy that leverages Training Dynamics and allows more informative samples to be combined for generating new data samples. |
| Outcome: | The proposed method achieves competitive performance using a smaller subset of training data compared with strong baselines and yields lower expected calibration error on the pre-trained language model, BERT, on both in-domain and out-of-domain settings. |
Easy to Decide, Hard to Agree: Reducing Disagreements Between Saliency Methods (2023.findings-acl)
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| Challenge: | A popular approach to unveiling the black box of neural NLP models is to leverage saliency methods, which assign scalar importance scores to each input component. |
| Approach: | They propose to use saliency methods to evaluate whether an explanation is faithful and argue that Pearson-r is a better-suited alternative to rank correlation. |
| Outcome: | The proposed methods exhibit weak rank correlations even when applied to the same model instance and advocated for alternative diagnostic methods. |
Plug-and-Play Data Module for Code RL: Adaptive Ambiguity Replay (2026.findings-acl)
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Jianqing Zhang, Wei Xia, Zhezheng Hao, Hong Wang, Hande Dong, Qiang Lin, Yang Liu, Jian Cao, Qiang Yang
| Challenge: | Existing approaches to reinforcement learning (RL) rely on static, in-epoch metrics that overlook training dynamics, often introducing low-utility or outdated data. |
| Approach: | They propose a plug-and-play module that prioritizes cross-epoch ambiguous samples to neutralize the noise from stale experiences. |
| Outcome: | Extensive experiments on nine LLMs show that Adaptive Ambiguity Replay outperforms state-of-the-art baselines on real-world code editing tasks. |
RPDR: A Round-trip Prediction-Based Data Augmentation Framework for Long-Tail Question Answering (2025.emnlp-main)
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| Challenge: | Long-tail question answering presents significant challenges for large language models due to limited ability to acquire and accurately recall less common knowledge. |
| Approach: | They propose a data augmentation framework that selects high-quality easy-to-learn training data to enhance dense retrieval models. |
| Outcome: | The proposed framework improves on two long-tail retrieval benchmarks, PopQA and EntityQuestion, and shows that it outperforms existing retrievers on extremely long-tailed questions. |