Papers with generative reward models
PIRA: Preference-Oriented Instruction-Tuned Reward Models with Dual Aggregation (2026.findings-eacl)
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| Challenge: | Existing approaches to align large language models with human preferences are limited by their large-scale annotation and prone to reward overoptimization. |
| Approach: | They propose a training paradigm that integrates three complementary strategies to address these challenges by reformulating question–answer pairs into preference-task instructions, averaging the rewards aggregated from diverse preference- task instructions for each sample, and a balancing outputs from the value head under different dropout rates. |
| Outcome: | Experiments on public datasets show that PIRA improves performance considerably, enhances generalization, and effectively mitigates reward overoptimization. |
LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing (2026.eacl-long)
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| Challenge: | a single prompt can inspire countless valid stories, making objective verification impossible. |
| Approach: | They propose a large-scale benchmark for creative writing evaluation using a reddit corpus and a 2,480-pair test set. |
| Outcome: | The proposed model outperforms existing OTS judges and generative reward models in the evaluation of creative writing. |
CE-RM: A Pointwise Generative Reward Model Optimized via Two-Stage Rollout and Unified Criteria (2026.findings-acl)
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| Challenge: | Existing studies have shown that rule-based evaluation methods are ineffective for open-ended natural language generation. |
| Approach: | They propose a pointwise generative reward model with a dedicated two-stage rollout method and unified query-based criteria that can be trained with 5.7K high-quality data. |
| Outcome: | The proposed model achieves superior performance on diverse reward model benchmarks, especially in Best-of-N scenarios, and delivers more effective improvements in downstream RL practice. |
From Scores to Preferences: Redefining Evaluation Paradigm for Speech Quality Reward Modeling (2026.findings-acl)
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Yifei Cao, Changhao Jiang, Jiabao Zhuang, Jiajun Sun, Ming Zhang, Zhiheng Xi, Hui Li, Shihan Dou, Yuran Wang, Yunke Zhang, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Experimental results show that the MOS-aware GRM significantly improves fine-grained speech quality discrimination. |
| Approach: | They propose a MOS-aware reward model that incorporates MOS gap into reward function during reinforcement learning. |
| Outcome: | The proposed model significantly improves fine-grained speech quality discrimination. |