Small open-weight models struggle at long-form creative writing: their generated stories either fall far short of the requested length, or their quality significantly degrades as length increases, especially when compared to frontier models. We present **POLARIS** (**P**olicy **O**ptimization with **L**LM-as-a-judge rewards and **A**nchored-**R**eference **I**njection for **S**torywriting), **a lower-compute GRPO recipe** with two key ingredients: a frontier LLM judge with a **structured *Story Quality* rubric** as the online reward, and **human-reference injection (HRI)**, where a teacher-forced human-written story serves as a high-reward anchor within each GRPO group. By applying our training recipe to Qwen3.5-9B, using a dataset of ∼1.4K prompt–story pairs derived from 100 short-story anthologies and 4 A100 GPUs, we obtain **POLARIS-9B**. Across five benchmarks spanning in-distribution and out-of-distribution prompts and rubrics, POLARIS-9B is **competitive with much larger open-weight models** while following length instructions more closely. A blinded human evaluation confirms that POLARIS-9B is preferred to the base Qwen3.5-9B and on par with Qwen3.5-27B. Despite training only on stories up to 4k words, POLARIS-9B preserves quality on prompts requesting stories up to 3× the training length, a regime where most open-weight models degrade substantially in quality, length adherence, or both. More broadly, our results suggest that **length generalization is a meaningful stress test for creative-writing models** and a useful lens for distinguishing otherwise close models.

The most effective techniques to detect LLM-generated text rely on inserting a detectable signature—or watermark—during the model’s decoding process. Most existing watermarking methods require access to the underlying LLM’s logits, which LLM API providers are loath to share due to fears of model distillation. As such, these watermarks must be implemented independently by each LLM provider. In this paper, we develop PostMark, a modular post-hoc watermarking procedure in which an input-dependent set of words (determined via a semantic embedding) is inserted into the text after the decoding process has completed. Critically, PostMark does not require logit access, which means it can be implemented by a third party. We also show that PostMark is more robust to paraphrasing attacks than existing watermarking methods: our experiments cover eight baseline algorithms, five base LLMs, and three datasets. Finally, we evaluate the impact of PostMark on text quality using both automated and human assessments, highlighting the trade-off between quality and robustness to paraphrasing. We release our code, outputs, and annotations at https://github.com/lilakk/PostMark.