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)
Table with columns: Model, ID (1–4k), Near OOD (4–8k), Far OOD (8–12k), Aggregate, Slope| Model | ID (1–4k) | Near OOD (4–8k) | Far OOD (8–12k) | Aggregate | Slope |
|---|
| POLARIS-9B | 57.4 | 48.2 | 44.1 | 52.1 | −3.0 |
| POLARIS-no-HRI-9B | 56.5 | 47.0 | 37.7 | 49.7 | −3.8 |
| Qwen3.5-9B (base) | 35.1 | 8.7 | −11.8 | 18.5 | −10.8 |
| Qwen3.5-27B | 51.5 | 38.7 | 24.6 | 42.8 | −5.9 |
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)
Table with columns: Model, ID (1–4k), Near OOD (4–8k), Far OOD (8–12k), Aggregate| Model | ID (1–4k) | Near OOD (4–8k) | Far OOD (8–12k) | Aggregate |
|---|
| POLARIS-9B | 63.1 | 57.5 | 54.3 | 59.8 |
| POLARIS-no-HRI-9B | 62.1 | 55.7 | 51.6 | 58.2 |
| Qwen3.5-9B (base) | 50.2 | 37.2 | 30.3 | 42.6 |
Length adherence (generated / requested word count)
Table with columns: Model, ID (1–4k), Near OOD (4–8k), Far OOD (8–12k), All| Model | ID (1–4k) | Near OOD (4–8k) | Far OOD (8–12k) | All |
|---|
| POLARIS-9B | 0.99 | 0.87 | 0.72 | 0.90 |
| POLARIS-no-HRI-9B | 0.94 | 0.86 | 0.70 | 0.87 |
| Qwen3.5-9B (base) | 1.09 | 0.96 | 0.88 | 1.01 |
OOD benchmarks
Table with columns: Model, WritingBench (D4), LongBench-Write, EQ-Bench Creative| Model | WritingBench (D4) | LongBench-Write | EQ-Bench Creative |
|---|
| POLARIS-9B | 7.9 | 81.2 | 70.3 |
| POLARIS-no-HRI-9B | 7.8 | 82.1 | 69.7 |
| Qwen3.5-9B (base) | 6.8 | 67.1 | 59.2 |
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
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.
Table with columns: Setting, Value, Notes| Setting | Value | Notes |
|---|
temperature | 0.4-1.0 | Lower temperatures (0.4-0.6) recommended for long-form story writing |
top_p | 0.95 | |
top_k | 20 | |
repetition_penalty | 1.0-1.10 | |
|
Prompting
it is recommended to include an explicit length request in the prompt:
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.
Table with columns: Parameter, Value| Parameter | Value |
|---|
| Base model | Qwen3.5-9B |
| Training algorithm | GRPO |
| Training data | ~1,388 prompt–story pairs from 100 short-story anthologies |
| Max reference length | 4,000 words |
| GPUs | 4× A100 80GB |
| Training time | ~48 hours |
| Compute cost | ~$400 |
| Judge cost | ~$60 (Gemini 3 Flash, flex tier) |
| Training steps |
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},
}
