實現零錯誤解決百萬步大型語言模型任務

摘要

大型語言模型在推理能力、洞察深度和工具運用方面已取得顯著突破，但將這些能力串聯成由人類、組織及社會常規執行的規模化延伸流程，至今仍難以實現。模型存在持續性錯誤率使其無法擴展：例如近期在漢諾塔基準領域的實驗顯示，該流程在最多數百步後必然會偏離正軌。因此，儘管LLM研究仍常以依賴邏輯步驟較少的任務作為基準，學界正日益關注LLM執行長週期任務的能力（或缺陷）。本文提出首個能無差錯完成超百萬步LLM任務的MAKER系統，其理論可擴展性遠超此規模。該方法通過將任務極致分解為可由專注型微代理處理的子任務，其產生的高度模塊化特性使多代理投票機制能在每一步驟實施誤差校正。這種極致分解與誤差校正的結合實現了規模化擴展。由此表明，與其依賴現有LLM的持續改進，採用大規模分解式代理流程或許能為組織及社會層面的問題提供高效解決路徑。

English

LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by humans, organizations, and societies has remained out of reach. The models have a persistent error rate that prevents scale-up: for instance, recent experiments in the Towers of Hanoi benchmark domain showed that the process inevitably becomes derailed after at most a few hundred steps. Thus, although LLM research is often still benchmarked on tasks with relatively few dependent logical steps, there is increasing attention on the ability (or inability) of LLMs to perform long range tasks. This paper describes MAKER, the first system that successfully solves a task with over one million LLM steps with zero errors, and, in principle, scales far beyond this level. The approach relies on an extreme decomposition of a task into subtasks, each of which can be tackled by focused microagents. The high level of modularity resulting from the decomposition allows error correction to be applied at each step through an efficient multi-agent voting scheme. This combination of extreme decomposition and error correction makes scaling possible. Thus, the results suggest that instead of relying on continual improvement of current LLMs, massively decomposed agentic processes (MDAPs) may provide a way to efficiently solve problems at the level of organizations and societies.

實現零錯誤解決百萬步大型語言模型任務

Solving a Million-Step LLM Task with Zero Errors

摘要

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