Open-sourcing AstaBrief, the fast report-generation model in Asta

Ai2 has open-sourced AstaBrief 8B, a Qwen3-8B-based model designed to generate cited scientific literature reports from research questions and retrieved excerpts, alongside its weights, training data, and a local PDF-report workflow. Available as Asta’s Fast mode, it produces reports in a single pass rather than through the slower multistep, section-by-section Claude-powered Thinking mode, averaging 51.1 seconds per report versus 178.5 seconds. The model was trained using 47,000 supervised examples derived from filtered real scientific queries and 6,000 preference pairs judged by GPT-4.1 and DeepSeek-R1, with an emphasis on evidence grounding, relevance, report structure, and citation support. Ai2 found that filtering training reports for citation density yielded especially strong improvements, while DPO further improved performance toward that of its proprietary pipeline and DR Tulu in development evaluations. The organization notes that the reported comparisons reflect 2025 models and methods rather than current frontier performance, but argues that the training-data, attribution-filtering, and efficient-serving lessons may generalize. Early Asta usage suggests Fast mode receives feedback comparable to Thinking mode, and open weights enable institutions to run the model locally for sensitive or unpublished research; future work will explore richer preference learning, retrieval-augmented reinforcement learning, multi-tool capabilities, broader data sources, and evaluations of whether reports preserve the scope and strength of scientific evidence.

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