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• Apriel-Reasoner uses an adaptive domain sampling mechanism that preserves target domain ratios despite heterogeneous rollout dynamics.method 90%
• Apriel-Reasoner employs a difficulty-aware extension of the standard length penalty that encourages longer reasoning for difficult problems and shorter traces for easy ones.method 90%
• Apriel-Reasoner improves over Apriel-Base on AIME 2025, GPQA, MMLU-Pro, and LiveCodeBench benchmarks.result 90%
• Apriel-Reasoner produces 30-50% shorter reasoning traces compared to Apriel-Base.result 90%
• Apriel-Reasoner matches strong open-weight models of similar size at lower token cost.result 85%
• Apriel-Reasoner was trained with a strict 16K-token output budget but generalizes to 32K tokens at inference.technical 90%
• Joint optimization across domains poses significant challenges due to varying rollout length, problem difficulty and sample efficiency.technical 85%
• Models with long chain-of-thought traces increase inference cost and latency, making efficiency critical for practical deployment.market 80%

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