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POLARIS trains small models for better long-form story writing

Researchers have developed POLARIS, a new training method designed to improve the long-form creative writing capabilities of smaller open-weight language models. This method utilizes a frontier LLM as a judge with a structured quality rubric and incorporates human-written story references as high-reward anchors during training. Applied to Qwen3.5-9B, the resulting POLARIS-9B model demonstrates competitive performance against larger models and shows improved adherence to length instructions, even for stories exceeding its training length. AI

IMPACT Enhances the creative writing capabilities of smaller, more accessible language models, potentially democratizing advanced AI content generation.

RANK_REASON The cluster contains an academic paper detailing a new method for training language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

POLARIS trains small models for better long-form story writing

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The cluster contains an academic paper detailing a new method for training language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rishanth Rajendhran, Jenna Russell, Mohit Iyyer, John Frederick Wieting ·

    POLARIS: Guiding Small Models to Write Long Stories

    arXiv:2606.04095v1 Announce Type: cross Abstract: 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 front…