WEval and WRL introduce fine-grained benchmarking and requirement-selective sample construction for training writing reward models, yielding substantial gains on writing benchmarks with strong generalization.
Suri: Multi-constraint instruction following for long-form text generation
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LongWriter-Zero applies RL from a base model with specialized rewards for length, quality, and structure to outperform SFT baselines and larger models on long-writing benchmarks.
MAGIC-HMO is a multi-agent framework that treats Chinese short-form creative NLG as heterogeneous multi-objective optimization over personalized constraints plus explanation reliability and outperforms baselines on a baby-naming benchmark.
citing papers explorer
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From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Tasks
WEval and WRL introduce fine-grained benchmarking and requirement-selective sample construction for training writing reward models, yielding substantial gains on writing benchmarks with strong generalization.
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LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning
LongWriter-Zero applies RL from a base model with specialized rewards for length, quality, and structure to outperform SFT baselines and larger models on long-writing benchmarks.
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Chinese Short-Form Creative Content Generation via Explanation-Oriented Multi-Objective Optimization
MAGIC-HMO is a multi-agent framework that treats Chinese short-form creative NLG as heterogeneous multi-objective optimization over personalized constraints plus explanation reliability and outperforms baselines on a baby-naming benchmark.