Thinking of ACE? We Can Do It with Fewer Tokens
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Hugging Face presents ALTK-Evolve, a system that teaches AI agents to use tools more reliably by saving and reusing previous mistakes and successes.
It resembles an earlier system called ACE, but differs in how it delivers the lessons: while ACE sends all learned lessons every time the agent does something, ALTK-Evolve sends only the most relevant ones. On the same task, ALTK-Evolve achieves equal or better results than ACE while using only one-seventh of the tokens that ACE requires — making it faster and cheaper to run.
ACE names the failure modes precisely: brevity bias — optimization collapsing toward short, generic instructions — and context collapse — a model asked to rewrite its whole context each step summarizing the detail away.
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Vibekollen prepared this summary with AI from the original publication. The content belongs to Hugging Face.
