From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Tasks
Large language models have achieved remarkable progress in text generation but still struggle with generative writing tasks. In terms of evaluation, existing benchmarks evaluate writing reward models coarsely and fail to measure performance from the perspective of specific requirements. In terms of training, existing training methods either use LLM-as-a-judge approaches or train coarse-grained reward models, lacking fine-grained requirement-adherence reward modeling. To address these issues, we propose a fine-grained evaluation pipeline WEval for writing reward models and a fine-grained reinforcement learning training framework WRL. The evaluation data of WEval covers multiple task catego...
| Authors | Qingyu Ren, Tianjun Pan, Xingzhou Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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