Writing-RL applies adaptive curriculum RL with pairwise rewards and dynamic scheduling to enhance long-form writing in 7B LLMs over SFT baselines and shows generalization to long-input reasoning tasks.
In our experiment, we use the proximal policy optimization (PPO) (Schul- man et al., 2017) algorithm with generalized advan- tage estimation (GAE) as the advantage estimator
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Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning
Writing-RL applies adaptive curriculum RL with pairwise rewards and dynamic scheduling to enhance long-form writing in 7B LLMs over SFT baselines and shows generalization to long-input reasoning tasks.