New method distills reasoning skills into language models, cutting token costs

Researchers have developed a method to improve the efficiency of reasoning in language models by distilling knowledge into compact natural-language skills. This approach amortizes the cost of reasoning, which typically requires 3-6x more output tokens, by compiling shared procedures from existing trajectories into a skill that is then injected into a non-reasoning model's system prompt. Across four agentic benchmarks, this technique recovered 55%-100%+ of the reasoning gap for GPT-5.4-mini, often outperforming the reasoning mode while significantly reducing token usage. AI

IMPACT This technique could significantly reduce the computational cost of complex reasoning tasks for AI agents, making them more efficient and accessible.

RANK_REASON This is a research paper detailing a novel method for improving language model efficiency. [lever_c_demoted from research: ic=1 ai=1.0]

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• ALFWorld
• arXiv
• GPT 5.4 Mini
• Hugging Face
• Reason Wide, Not Deep: Amortizing the Reasoning Premium into Distilled Skills
• SpreadsheetBench-Verified
• tau^2-Bench

• paper
• model release

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