POLARIS-9B

POLARIS-9B is a 9B parameter model for long-form English story generation, trained with a reinforcement-learning recipe on top of Qwen3.5-9B. Despite training only on stories up to 4k words, it maintains rubric quality on prompts requesting stories up to 12k words — 3× its training length. In pairwise evaluation it ranks above all tested open-weight models on EQ-Bench Creative Writing Elo, and a blinded human study finds it preferred to Qwen3.5-9B (67.5% winrate) and on par with Qwen3.5-27B (51.2% winrate).

The training recipe — POLARIS (Policy Optimization with LLM-as-a-judge rewards and Anchored-Reference Injection for Storywriting) — uses two core components: a frontier LLM judge with a structured 16-dimension Story Quality rubric as the online reward, and human-reference injection (HRI), where a teacher-forced human-written story is inserted into each GRPO group as a high-reward anchor. The full training run costs approximately $500 in compute and judge calls (4×A100 80GB, ~48 hours).

Results

Pairwise Elo (Gemini 3 Flash judge, dual-position)

Story Quality by requested length (GPT-5.4 judge, 180 held-out prompts)

Length ratio is generated / requested word count (1.0 = exact). Gemma 4 31B maintains quality at long lengths by writing substantially shorter stories than requested; POLARIS-9B is among the few open-weight models in our comparison that largely avoids quality collapse, length runaway, and severe under-generation at far-transfer lengths.

Human evaluation (60 prompt–generation pairs, blinded, two annotators)

Annotator comments most often highlight stronger atmosphere, voice, and scene realization relative to the base model.

Intended Use

• Long-form story generation (short stories, flash fiction, narrative scenes etc)
• Creative writing (essays, book reviews, podcast scripts etc)

POLARIS-9B is trained on short-story anthology data and transfers well to related narrative tasks. Within WritingBench, it performs strongest on categories closest to its training distribution: character design, fan fiction, novel manuscript, and podcast scripting.

Out-of-Scope Use

• Factual or knowledge-intensive writing where correctness matters
• Legal, medical, or financial content
• Reproducing or recovering the withheld training stories

Usage

POLARIS-9B uses extended thinking during generation. Enable thinking and provide adequate token budget for long stories.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "rishanthrajendhran/POLARIS-9B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = (
    "Write a 2000-word story about an archivist who discovers that missing "
    "library books are returning with handwritten notes from the future."
)

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    # Enable thinking — important for quality
    enable_thinking=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=8192,
    do_sample=True,
    temperature=0.6,
    top_p=0.95,
    top_k=20,
    repetition_penalty=1.10,
)

# Strip the thinking trace; return only the story
generated = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(generated)

Recommended Generation Settings

These match the settings used in the paper's main evaluation.

Thinking token budget counts toward max_new_tokens but is stripped before evaluation. If the model is producing very short stories, increasing max_new_tokens is usually the first thing to try.

Prompting

It is recommended to include an explicit length request in the prompt. POLARIS-9B was trained with length-stratified prompts and uses the requested word count to calibrate output length. Example:

Write a 3000-word story about [premise].

At far-transfer lengths (8–12k words), the model undershoots somewhat (length ratio ≈ 0.72 aggregated across the far-OOD bucket). This is still substantially better than much larger open-weight models that write 0.36× the requested length while appearing to maintain quality scores.

Known Limitations

Stylistic overloading. The model can push too hard on specificity, jargon, or figurative density, making prose feel effortful to read even when individual sentences are well-crafted. Annotators flagged this as a recurring pattern.

Local coherence failures. Contradicting details and confusing transitions may appear across examples, particularly in longer stories. The narrative usually stays on track, but individual passages may lose logical consistency.

Length undershooting at far transfer. On prompts requesting 8–12k words, the model generates approximately 72% of the requested length on average. Quality is preserved relative to other open-weight models, but the full length target is not reliably met.

Story-writing distribution. The training data is short-story anthology fiction (literary realism, horror/gothic, sci-fi, regional/folk writing). Performance on non-narrative writing categories (biography, essays, book reviews) is noticeably weaker.

Single-seed training. The reported checkpoint reflects one training run. Seed-to-seed variance has not been characterized.

Training

The human-written stories used in training are derived from commercially purchased anthologies and are not released. The associated prompt dataset is released separately.

Citation

@misc{rajendhran2026polarisguidingsmallmodels,
      title={POLARIS: Guiding Small Models to Write Long Stories}, 
      author={Rishanth Rajendhran and Jenna Russell and Mohit Iyyer and John Frederick Wieting},
      year={2026},
      eprint={2606.04095},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.04095}, 
}