Comparison with POLARIS-9B

The gap between this model and POLARIS-9B is small at in-distribution lengths and grows at longer requested lengths, consistent with HRI's role in maintaining gradient pressure toward stronger writing as generation extends beyond the training range.

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

Slope is the linear fit across the six length buckets (points per step). A steeper negative slope indicates faster quality degradation as requested length increases.

EQ-Bench Longform by requested length (GPT-5.4 judge, uniform aggregation)

Length adherence (generated / requested word count)

OOD benchmarks

On OOD benchmarks the two variants are essentially tied; the HRI advantage is concentrated at long in-distribution lengths where narrative coherence and arc completion are required over many thousands of tokens.

Intended Use

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

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

python
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from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "rishanthrajendhran/POLARIS-no-HRI-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=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,
)

generated = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(generated)

Recommended Generation Settings

Identical to POLARIS-9B.

Prompting

it is recommended to include an explicit length request in the prompt:

markdown
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Write a 3000-word story about [premise].

At far-transfer lengths (8–12k), this model undershoots more than POLARIS-9B (length ratio ≈ 0.70 vs 0.72). For generation targets above 6k words, POLARIS-9B is the recommended variant.

Known Limitations

The same qualitative failure modes present in POLARIS-9B apply here — stylistic overloading and local coherence failures — since both models share the same base, training data, and reward. The key additional limitation of this variant relative to POLARIS-9B:

Steeper quality degradation at long lengths. Story Quality slope is −3.8 vs −3.0 for POLARIS-9B. At 8–12k words, the gap to POLARIS-9B is 6.4 Story Quality points, compared to ~1–2 points at in-distribution lengths. If your use case involves prompts requesting long stories, POLARIS-9B is the better choice.

Training

Identical to POLARIS-9B except for the group composition.

Citation

bibtex
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@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}, 
}